
==== Front
Sci Rep
Sci Rep
Scientific Reports
2045-2322
Nature Publishing Group UK London

39251705
71938
10.1038/s41598-024-71938-7
Article
Evaluating information management system in epidemic infectious diseases in Iran
http://orcid.org/0000-0003-1564-5840
Samimi Susan 1
http://orcid.org/0000-0003-2692-7771
Zarei Javad 1
http://orcid.org/0000-0002-7524-5681
Jamshidnezhad Amir jamshidnejad@ajums.ac.ir

1
http://orcid.org/0000-0003-2290-2833
Fadaei Dehcheshmeh Nayeb 2
1 https://ror.org/01rws6r75 grid.411230.5 0000 0000 9296 6873 Department of Health Information Technology, School of Allied Medical Sciences, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
2 Department of Public Health, Shoushtar Faculty of Medical Sciences, Shoushtar, Iran
9 9 2024
9 9 2024
2024
14 2102014 1 2023
2 9 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Health information management is a vital and constructive component of the health system, refers to the process of producing and collecting, organising and storing, analysing, disseminating and using information. The aim of this study was to evaluate the strengths and weaknesses of the information management system in epidemic infectious diseases in Iran, specifically focusing on the registration, reporting, quality, confidentiality, and security of infectious disease data. This assessment was conducted from the perspective of policymakers and experts responsible for data registration and reporting. After examining the processes of registering and reporting infectious disease data and interviewing experts, a researcher-designed questionnaire was prepared to evaluate the infectious disease information management system. To assess the content validity of the Content Validity Index and Content Validity Ratio Index, a questionnaire was utilized. The reliability of the questionnaire was confirmed using Cronbach's alpha. By employing purposeful sampling and adhering to the inclusion criteria, 150 participants were included in the study. Questionnaires were distributed via email, WhatsApp, or Telegram to employees at various levels of Iran's health and treatment systems who were responsible for registering and reporting infectious disease data. The study encompassed 100 participants who successfully concluded the research. The results highlight that the key strength of healthcare data registration lies in its ability to "depict the epidemic curve during outbreaks of infectious diseases." Conversely, a notable weakness was the "insufficient collaboration from non-academic sectors (e.g., clinics, private laboratories) in registering and reporting infectious diseases. The present study's findings suggest that the issue lies not in the framework itself, but rather in the execution and functionality of the strategies. We can cultivate a repository of reliable and beneficial data by incorporating initiatives like training programs, enforcing regulations with consequences for inadequate data documentation, offering both material and motivational rewards, and streamlining all data collection and reporting systems.

Keywords

Routinely collected health data
Information management systems
Public reporting of healthcare data
Communicable diseases
Subject terms

Health policy
Health services
Public health
Data processing
Ahvaz Jundishapur University of Medical Sciences, Ahvaz, IranU-00086 issue-copyright-statement© Springer Nature Limited 2024
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pmcIntroduction

Infectious disease epidemics have always been one of the main causes of death in the human population. Some of the infectious disease epidemics that have occurred throughout history include the Sars1–3 , the swine flu4,5, the MERS1,2 and the Zika6–10. A review of the history of Iran reveals the significant impact of infectious diseases, such as cholera, on the population, with documented deaths reaching thousands11. The most recent epidemic that affected the whole world, the coronavirus (SARS-CoV-2), which was named COVID-19, was declared a pandemic by the World Health Organisation in March 202012. Epidemics have caused severe damage to human society, and despite major medical advances in the prevention and treatment of infectious diseases, infectious diseases with the capacity to cause epidemics are unfortunately still a health problem throughout the world13. The experience of recent epidemics has shown the importance of utilising information management and information systems to manage and prevent the outbreak of epidemics 14.

Information of any origin and source and of any form and format must be managed15. Information management, as one of the essential and constructive components of a healthcare system16, refers to the process of producing and collecting, organising and storing, analysing, disseminating and using information17. The information management system in Iran has problems such as weakness of management for collection, organisation and storage of coherent data, lack of standard forms at national level, lack of management for coherent and centralised collection and storage, lack of rules and regulations and national standard definitions and indicators related to health information management system, lack of comprehensive and coordinated analysis of health information at country level, lack of effective decision making and timely health information18–20. Another challenge for information management in Iran is the absence of a systematic approach to collecting, organizing, and analyzing information at the national level18.

The production of large amounts of incorrect and inaccurate data has been very evident in past epidemics21. During the recent epidemic of infectious diseases, despite the collection and reporting of data through various systems22, some medical universities in the country decided to develop local systems to collect and report the required data due to the low quality of the data produced23,24. Such problems have also been raised in other countries25.

The studies conducted in the discussion of infectious disease programs revealed a number of problems and challenges, including the collection of data with scattered and non-interoperable systems, the lack of a clinical data repository at the national level, which leads to data redundancy, and the accumulation of a large amount of data, some of which is unnecessary, unusable, and lacks the necessary quality26. Identifying problems and barriers and overcoming them will lead to less low-quality data being produced, and this will only be possible through evaluation27. Therefore, continuous evaluation of infectious disease control programmes has been strongly emphasised in studies28,29.

Given the critical importance of access to important and high-quality data for critical decision-making during a pandemic and the impact of various factors on data quality in data collection and reporting, an evaluation can lead to identifying and addressing the weaknesses and recognising the strengths of the registration and reporting system we have during the epidemic. High quality data will help us to be better prepared in the future and minimise the number of deaths and injuries. On the other hand, conducting this assessment by experts responsible for the daily collection and reporting of infectious disease data will not only improve their performance, but also provide a comprehensive understanding of the strengths and weaknesses of the system. Therefore, the aim of this study is to evaluate the information management system during an infectious disease epidemic in Iran.

Materials and methods

Study design

The present study was an applied research conducted in 2022, consisting of five stages with no interventional or clinical trial method. All methods in this research were carried out by the relevant guidelines and regulations and the study protocols were approved by by the Ethics Committee of Ahvaz Jundishapur University of Medical Science, Iran with the reference number of IR.AJUMS.REC.1400.153. In the first phase, the necessary information was gathered by visiting relevant centers, observing and reviewing the process of recording and reporting infectious data, and interviewing the experts responsible for this matter. Then, the data collected from the previous stage was analyzed by the expert panel using a Likert scale. The following explains the step-by-step process of completing the work.

Review of data sources

This study was conducted in Bushehr province in the southern region of Iran by collecting information from healtcare system. For this purpose, as a first step, all registration and reporting procedures (paper and electronic) were observed and all forms and policies for communicable diseases reporting protocol, especially COVID-19, were reviewed in the health and treatment departments of the University of Medical Sciences, including Vice-Chancellor of Health, Vice-Chancellor of treatment, Counties Health and Treatment Network, university hospital considered the main hospital for admitting patients with COVID-19, Comprehensive Urban Health Centers, Reference laboratories to perform COVID-19 diagnostic tests, Health houses and Private Centres. All identified weaknesses and strengths were documented. During this stage, in order to identify the main weaknesses and strengths, an interview was held at this stage with the experts responsible for collecting and reporting the data.

Participants included: communicable disease control experts (working in the headquarters of the Vice President of Health, Counties Health and Treatment Network and Comprehensive Urban Health Centers) and managers working in Vice President of Treatment (Department of Statistics and Medical Records) of Bushehr and Ahvaz Universities of Medical Sciences. They were not only familiar with healthcare system but also had sufficient experience at different levels of the healthcare system system and infectious diseases (at least 2 years of work experience). At this stage, sources were continuously reviewed and experts were interviewed until data saturation was achieved and no new strengths or weaknesses emerged from the sources or the experts' discussions.

In this step, the method of purposive sampling was applied. This method is used when the researcher intends to include the views, opinions and attitudes of specific individuals in the resulting information. At this stage, 12 people were interviewed according to the criteria. In-depth semi-structured interviews were conducted to collect data. After informed and verbal consent was obtained from the participants and the objectives of the research were explained, a face-to-face interview was conducted at the participants' workplace at an agreed time. The interviews were recorded using a digital recorder. They lasted between 30 and 45 min and the data was collected over a period of three months. The information from the interviews was then analysed using content analysis.

After the weaknesses and strengths were fully extracted, a group discussion was held in two sessions with four experts and faculty members of Ahvaz University of Medical Sciences (PhD in Health Information Management, PhD in Medical Informatics, PhD in Public Health Science and Infectious specialist) to reach a complete overview and consensus.

Questionnaire development

The sources review provided a working basis for developing a questionnaire to elicit the expert panel's individual opinions about strengths and weaknesses of the health information management system. The experts who participated in the survey were asked to assign a priority value to each of the questionnaire items using a five-point Likert scale. Based on this scale, a score of 0 represented the “Strongly disagree” and a score of 4 represented the “Strongly agree”. Finally, participants were asked to suggest new items that were not listed. The face validity of the questionnaire was confirmed by experts. For this purpose, the questionnaire was given to two experts with a doctorate in health information management. In order to determine the content validity, it was given to 13 experts, including academic teachers, epidemiologists, specialists and experts in infectious diseases from different levels of the healthcare system, including the Ministry of Health, the deputy healthcare of universities, the headquarters of the provincial health network of cities and provinces. After collecting opinions and calculating the content validity index (CVI) and content validity ratio index (CVR) of the questionnaire, the content validity of the questionnaire was confirmed. Reliability of each section of the questionnaire was also confirmed by Cronbach’s alpha of 0.84, 0.84, and 0.89 for the three dimensions of weaknesses (a, b, and c), respectively, and 0.73 and 0.89 for the two dimensions of strengths (d and e), respectively. The proposed questionnaire contained tree parts: the first part of the questionnaire collected the personal characteristics of the participants, including sex, age, educational level, field of study, place of work, and work experience. The questionnaires were anonymous and the participants’ identification information (name and ID code) were not asked. The second part of the questionnaire focused on weaknesses of the system, which included three main dimensions: a) Registration and reporting policies, b) Design of current information systems for registration and reporting, and c) Training and supervision of people in charge of data registration and reporting. In the third part of the questionnaire, the strengths were asked, which included two main dimensions: d) Registration and reporting policies and e) Design of current information systems for registration and reporting. The two later parts included 54 items. For each item in the questionnaire, five columns of “Strongly disagree”, “Disagree”, “I have no opinion” , “Agree” and “Strongly agree” with a score of 0–4, respectively, were considered. At the end of each dimension, a blank row was provided for experts to add suggestions. The final questionnaire was designed electronically on an Iranian platform, used for creating electronic questionnaires, called "Porsline".

Selection of participants

According to the study's objectives, participants were selected for opinion polls based on their specialized knowledge, work experience during the COVID-19 pandemic, and their organizational position within Iran's health system. Therefore, it was necessary to select individuals who are most familiar with the system for registering and reporting infectious diseases with pandemic potential in Iran. To identify these participants, we utilized the experiences of three specific groups directly involved in collecting and reporting patient data or controlling outbreaks during the COVID-19 pandemic:

The executive team of the COVID-19 registry system in Khuzestan province:

The registry system was implemented in Khuzestan province from April 2020 to September 2022. The details related to it have been mentioned in previous studies23,30.

Members of the provincial headquarters for the management of COVID-19:

The coordination between organizations and macro-policy making in response to the COVID-19 situation in each province was assigned to the provincial COVID-19 management headquarters. This headquarters was established under the supervision of the governor of each province, with the participation of the leaders of all organizations directly and indirectly involved in the management of COVID-19. In the present study, assistance was sought from some former members of the provincial COVID-19 management headquarters in Khuzestan province to select participants.

Members of the intra-university committee for the management of COVID-19 in medical sciences universities:

According to the structure of the health system in Iran, which is centered on medical sciences universities31, each province, in addition to the provincial headquarters for COVID-19 management, also had a dedicated committee within the relevant university/universities of medical sciences for controlling COVID-19. This committee was responsible for coordinating the activities of various vice-chancellors of the university, different health service centers, and health planning in response to the COVID-19 situation in the covered geographical area. Additionally, this committee reported to the provincial headquarters for the management of COVID-19. In this study, the recommendations of some former members of the intra-university committee for the management of COVID-19 at Ahvaz Jundishapur University of Medical Sciences address were used to select the participants.

Based on their opinions, a list of target experts was selected to participate in the study (Table 1).Table 1 Experts selected to participate in the study and inclusion and exclusion criteria for their selection.

Organization/target group	Department/unit/target expert	Inclusion criteria	Exclusion criteria	
Ministry of Health	Center of Communicable Disease Control, Center for Hospital Management and Clinical Services Excellence	At least 2 years of work experience in the relevant field

Work experience in managing COVID-19 during the pandemic

	Less than two years of work experience in the relevant field

Lack of engagement in COVID-19 management during a pandemic

	
Vice-Chancellor of Health at Medical Sciences Universities	Deputy health officer, Center/office of Communicable Disease Control	
Vice-Chancellor of treatment at Medical Sciences Universities	Deputy of treatment officer, Deputy of treatment manager, Office of management of diseases and diagnostic and treatment centers, the Administration of laboratories, Nursing management office, Department of Statistics and Health Information Technology	
Healthcare centers	Hospital	Hospital management team	
Urban health center (Primary care)	Director of the health center	
Experts	Infectious disease specialist	At least 2 years of work experience in the relevant field

Work experience in managing COVID-19 during the pandemic

Membership in the university faculty

	Less than two years of work experience in the relevant field

Lack of engagement in COVID-19 management during a pandemic

Non-faculty membership in the university

	
Health information management specialist	
Epidemiologist	
Health education specialist	

After identifying the desired experts to participate in the study, the next step involved sending questionnaires to these individuals. During summer 2022, the questionnaires were sent to the healthcare employees at different levels via email, WhatsApp or Telegram. The researcher obtained verbal and written consent from the participants to take part in the study. Obtaining verbal consent to send the questionnaire was achieved by explaining the objectives and benefits of the research in person to the participants, as well as by sending voice messages in online groups. At the beginning of the online questionnaire, the statement "I am participating in this research with full knowledge and consent" was provided, along with the options to select "Yes" or "No" to obtain written consent. In this manner, participants who responded positively were able to access the questionnaire content and complete it. Participants who selected "no" exited the software without viewing the questionnaire content. Therefore, informed consent was obtained from all participants for their participation in the study. At the end, 100 participants completed questionnaires. There was no response from 50 participants. Table 2 shows the demographic characteristics of the study participants.Table 2 The demographic characteristics of the study population.

Variable	Categories	Frequency (N = 100)	Percentage (%)	Mean	standard deviation	
Sex	Male	64	64			
Female	36	36			
Age (years)	 < 30	7	7	42.0	6.8	
30–39	33	33	
40–49	47	47	
 ≤ 50	13	13	
Educational level	BSc	35	35			
MSc	41	41			
MD	7	7			
Medical specialist	5	5			
PhD	12	12			
Infectious disease specialist	3	3			
Epidemiology	8	8			
Demography	2	2			
Health Information Management	9	9			
Field Of Study	Healthcare Management	10	10			
Health Education	9	9			
Health (Public, Environmental, Professional)	29	29			
Doctor of Medicine (M.D)	7	7			
Nursery	8	8			
Other	15	15			
Place of work	Comprehensive health service center	4	4			
Hospital	13	13			
Network of healthcare of the city	39	39			
Medical schools (School of Health, School of Allied Medical Sciences, and School of Medicine)	12	12			
University vice-chancellor headquarter of treatment	8	8			
University vice-chancellor headquarter of health	22	22			
Ministry of health	2	2			
ob experience	≤ 10 years	19	19	16.4	6.8	
11–20 years	55	55	
 > 20 years	26	26	

Survey of participants

To calculate the total score, the mean scores for each section's strengths and weaknesses were calculated separately. The resulting number was then converted into a percentage according to the following equation:Mean/4∗100

Mean/4*100After the initial ranking, if the percentage is less than 50 (average less than 2), the items were removed from the questionnaire. At this stage, all items received a percentage above 50% and all of them remained in the study.

Statistical analysis

The collected data were input into Microsoft Excel 2019 software, used for the statistical analyses. The results were described using descriptive statistics, including mean ± standard deviation (SD) for quantitative variables and frequency (percentage) for categorical variables.

Results

A total of 100 participants completed the study. The demographic characters of the participants are shown in Table 1. As shown; 64% were men; the mean age of participants was 42.0 years(± 6.8 SD); the majority (47%) were in the age range of 40–49 years and the least in the age range of < 30 years (7%). Most (29%) were educated in health subjects (general health, environmental health, and occupational health). Most (41%) had academic degree of MSc. The place of work in 39% was the healthcare network. Mean job experience of the participants was 16.4 (± 6.8 SD) years. (See Table 1).

The mean total scores of the weaknesses were 3.0, 3.07 and 2.9 for dimensions “a”, “b", and “c”, respectively; mean total scores of the strengths were 2.9 and 3.0 for ‘d” and “e”, respectively. Figure 1 shows a better illustration of these values.Fig. 1 Mean values of the scores in each of the dimensions of weaknesses and strengths.

Weaknesses of the information management system

In short, the weaknesses and important findings of each dimension are presented in Fig. 2.Fig. 2 Weaknesses of the information management system.

A detailed description of the mean and percentages of each item of the three dimensions of weaknesses are shown in Tables 3, 4, 5. In dimension “a”, the greatest mean and percentage were related to the “Inadequate cooperation of the non-academic sector (e.g., clinics, private laboratories) in the process of registering and reporting infectious diseases” (mean of 3.4 and percentage of 86.7) and the least to the “Absence of a comprehensive and unified protocol for data registering and reporting process in different healthcare centers” (mean of 2.2 and percentage of 55.2; Table 3).Table 3 Mean score and percentage of different items of registration and reporting policies (the first dimension of weaknesses).

Items	Mean	Percentage	
Absence of a systematic approach for risk assessment and management in the Ministry of Health for predicting how to register and report data during the epidemic of infectious diseases	2.4	62.2	
Absence of a comprehensive and unified protocol for data registering and reporting process in different healthcare centers	2.2	55.2	
Greater responsibility of the health sector than the treatment sector for registering and reporting c infectious disease data	3.2	80.2	
Weak communication between the treatment department (e.g. hospitals) and the relevant health center for unifying the approach of registering and reporting infectious diseases data	3.2	80	
Inadequate cooperation of the non-academic sector (e.g. clinics, private laboratories) in the process of registering and reporting infectious diseases	3.4	86.7	
Absence of a comprehensive legal approach to intercept the information of infected patients and monitor compliance with the quarantine of people infected with infectious diseases	3.1	77.5	
Failure to report the data of some infectious diseases in hospitals affiliated to the universities of medical sciences in the country by the relevant systems (e.g. HSE)	3.0	75	
Failure to use legal capacities and tools to deal with non-university sectors (private/non-governmental) in cases of not registering/reporting	3.2	81	
Lack of a systematic approach for sharing data between different sectors involved in disease management (e.g. health vice headquarters, medical vice headquarters, university and non-university hospitals, specialist doctors’ offices)	3.0	76.7	
Lack of easy and timely access of various organizations and bodies involved during an epidemic outbreak (disease peak) to the data required for the latest situation of the epidemic or peak occurred	2.9	72.5	
Simultaneous manual (paper) and electronic data registering and reporting in some centers and creation of data redundancy in the process of data registering and reporting	3.0	76	
Inadequate use of the experience and expertise of experts in the field of statistics and health information (e.g. experts in health information management, epidemiology, medical informatics) to help manage data collection during an epidemic or a peak of infectious diseases	3.0	76.2	
Lack of a systematic approach for quality control of information about data collected from different sources	3.0	75.5	
The low quality of collected data due to the existence of problems such as low registration, incomplete data registration, inconsistency between data, and inaccuracy of data during epidemic of infectious diseases	3.1	78	
Registration and reporting system are not society-based and people do not know and cannot report in necessary cases	3.1	79.5	

Table 4 Mean score and percentage of different items of Design of current information systems for registration and reporting (the second dimension of weaknesses).

Item	Mean	Percentage	
Using multiple and separate systems in healthcare sectors for simultaneous record and report data of infectious diseases and lack of a single and integrated system from the most peripheral level (health center) to the highest level of the healthcare sector (Ministry of Health) to record and report all infectious disease data	3.2	82.2	
Impossibility of data exchange (data sharing) between the systems used to record and report infectious disease data (Infectious Disease Management Center portal system, syndromic care system, SIB system)	3.1	77.5	
Lack of connection between the systems used in the health and medical department (infectious disease management portal, tuberculosis system, malaria system, MCMC system) with the civil registration system to verify the identity of patients	3.1	78.5	
Lack of infectious (possibility of data exchange) between the systems (Portal of the Infectious diseases Management Center, HSE, MCMC) with other important systems in the Ministry of Health, such as the SEPAS system and the death registration system	3.2	82.2	
Parallel working of the systems designed to register and report infectious disease data, simultaneous registration of patients’ data in several healthcare sector systems	3.2	81.7	
Discrepancy of statistics of recorded cases (death and disease) in the systems used to register and report infectious diseases (MCMC, HSE)	3.2	81.5	
Lack of user-friendly systems, designed to record and report infectious disease data	2.7	69.7	
Not using structured data elements (vocabulary standards) in defining the data registration fields in the systems used	2.8	70.7	
Failure to use controlling mechanisms during design of systems to reduce the possibility of user error, such as: Data-type check, simple range and constraint check, Cross-reference, data consistency check	2.8	72	
Absence of clinical data repository at the level of universities of medical sciences and the Ministry of Health to combine data collected from different systems	3.0	75.5	
The impossibility of access of some key stakeholders in medical sciences universities (such as specialist doctors, researchers, middle managers in university headquarters units, etc.) to the information of these systems, due to confidentiality and limited access to data of some of these systems	3.0	75.5	
Lack of appropriate management dashboards to analyze and display important information for senior managers in universities	2.9	73.2	
Weakness in the security of information in some of the systems (e.g. sharing of usernames and passwords of systems related to the registration and reporting of infectious diseases data by the personnel of healthcare centers due to a single username and password for each city to enter some of these systems, impossibility of defining access levels for different users in some systems, the absence of electronic signatures, etc.)	2.8	71.5	
Existence of problems related to infrastructure in some health centers such as lack of facilities and equipment, internet access problem	3.2	81.7	
Lack of access of the private sector/non-university/non-governmental sectors to some of the systems used to record and report infectious disease data	3.1	78.5	
Lack of access of registered users to some patient information such as diagnostic test results, underlying disease records, etc	3.0	76	

Table 5 Mean score and percentage of different items of training and supervision of people in charge of data registration and reporting (the third dimension of weaknesses).

Item	Mean	Percentage	
Lack of proper training of users in charge of registering and reporting infectious disease data in different healthcare centers	2.6	66.5	
Lack of the similar opinion about the method of data registering and reporting among the users in charge of registering and reporting data in different health centers	2.7	67.5	
Failure to predict incentive or penalty mechanisms to ensure correct data registering and reporting by users	3.2	82.2	
Absence of a systematic process to monitor the performance quality of users in charge of data registering and reporting in various healthcare centers	3.0	75.2	
Failure to send regular and periodic feedback from the higher levels of the health care system to those in charge of registering and reporting infectious diseases data at the lower levels of the health care system	2.7	67.5	
Lack of precise and clear determination of the role of different users in charge of registering and reporting data during an epidemic in different healthcare centers	2.7	69.5	
Lack of a systematic structure to communicate and share problems and experiences between users in charge of registering and reporting data in different healthcare centers	3.0	76.5	
Lack of trained people to collect statistical forms related to infectious diseases from private and non-governmental centers at the city or county level	3.0	76.5	
Not using experts in infectious diseases or people familiar with informational systems in the treatment department for registering and reporting infectious disease data	3	75	

In dimension “b”, the highest mean and percentage were related to the “Lack of communication (possibility of data exchange) between the systems (Portal of the Infectious diseases Management Center, HSE, MCMC) with other important systems in the Ministry of Health, such as the SEPAS system and the death registration system” and “Using multiple and separate systems in healthcare sectors for simultaneous record and report data of infectious diseases and lack of a single and integrated system from the most peripheral level (health center) to the highest level of the healthcare sector (Ministry of Health) to record and report all infectious disease data” (mean of 3.2 and percentage of 82.2) and the least to the “Lack of user-friendly systems, designed to record and report infectious disease data” (mean of 2.7 and percentage of 69.7; Table 4).

In dimension “c”, the highest mean and percentage were related to the “Failure to predict incentive or penalty mechanisms to ensure correct data registering and reporting by users” (mean of 3.2 and percentage of 82.2) and the least to the “Lack of proper training of users in charge of registering and reporting infectious disease data in different healthcare centers” (mean of 2.6 and percentage of 66.5; Table 5).

Strengths of the information management system

In short, the strengths and important findings of each dimension are presented in Fig. 3.Fig. 3 Strengths of the information management system.

The mean and percentages of the two dimensions of strengths (“d” and “e”) are shown in Tables 5 and 6. As shown in Table 6, the highest mean and percentage were related to the “The structure of the network system in Iran, and the existence of health units at the level of all cities and villages” (mean of 3.1 and percentage of 77.5) and the least to the “The existence of an independent management to control infectious diseases at the level of the Ministry of Health, the University of Medical Sciences and the healthcare network” (mean of 2.7 and percentage of 68.7; Table 6).Table 6 Mean score and percentage of different items of registration and reporting policies (the first dimension of strengths).

Items	Mean	Percentage	
The existence of an independent management to control infectious diseases at the level of the Ministry of Health, the University of Medical Sciences and the healthcare network	2.7	68.7	
The structure of the network system in Iran, and the existence of health units at the level of all cities and villages	3.1	77.5	
Existence of mandatory reporting system for infectious diseases	2.9	73	
Specific, separate and accurate definition of all infectious diseases subject to mandatory reporting and installing them in all government, non-government, and private centers for information, registration and timely reporting to the headquarters of the city health center	3.0	76.2	
Existence of a specific and separate schedule for registering and reporting the data of each of the infectious disease programs	2.9	74	

In dimension “e”, as shown in Table 7, the highest mean and percentage were related to the “Drawing the epidemic curve during an epidemic or outbreak of infectious diseases” (mean of 3.1 and percentage of 78.5) and the least to the “Conducting a descriptive and analytical study during an epidemic or outbreak of infectious diseases” (mean of 2.9 and percentage of 73.5; Table 7).Table 7 Mean score and percentage of different items of design of current information systems for registration and reporting (the second dimension of strengths).

Items	Mean	Percentage	
Registering data related to vaccination and follow-up of some infectious diseases (COVID-19) in the Integrated Health System (SIB)	3	75	
Web-based systems used to register and report diseases and ability of using at any time and place with any device (phone, computer, etc.)	3.0	75.5	
Monitoring and confirming the data related to the registration and reporting of infectious diseases data at all levels of the healthcare system by higher levels	3.0	75.2	
Forming a rapid response team during an epidemic or outbreak of infectious diseases	3.1	78.2	
Carrying out a risk assessment during an epidemic or outbreak of infectious diseases at the level of the health department	3	75	
Conducting a descriptive and analytical study during an epidemic or outbreak of infectious diseases	2.9	73.5	
Drawing the epidemic curve during an epidemic or outbreak of infectious diseases	3.1	78.5	
Depiction of a hypothesis during an epidemic or outbreak of infectious diseases to predict the course of the disease epidemic	3.0	77	
Submission of the final written report within 12 days after the end of the epidemic or outbreak of infectious diseases	2.9	74	

Discussion

The aim of this study was to evaluate the information management system for epidemic infectious diseases in Iran, specifically by analyzing the strengths and weaknesses of registering and reporting infectious disease data. In this study, the main weaknesses and strengths were identified and summarized in three dimensions for weaknesses (a, b, and c) and two dimensions for strengths (d and e). Subsequently, the perspectives of the healthcare employees responsible for data registering and reporting were collected and scored. A general look at the mean values of the five dimensions showed that they were closely clustered around 2.9–3.0, indicating the significance of all dimensions. Among detailed weaknesses, the inadequate cooperation of the non-academic sector in the process of registering and reporting infectious diseases data was the most significant weakness reported. This finding aligns with the study's hypothesis and is connected to the healthcare center structure in our country. Each health and treatment center is required to report its collected data to the city-level health center, and then submit the data to the University of Medical Sciences, and subsequently to the Ministry of Health. But, private clinics, labs, and hospitals that are not affiliated to medical universities may not collaborate with this system. Given that a significant number of patients in Iran seek treatment in the private sector, it is essential to prioritize involving the private sector in registering and reporting data27. Since the private sector does not receive financial support from the University of Medical Sciences, their non-cooperation is not expected. In fact, they have no obligation to record and report data. Therefore, mechanisms should be used to enhance motivation and encourage their participation and cooperation. For example, the contract or memorandum of understanding, registration of payment-based data, training, increasing awareness of the purposes of data registration and reporting, feedback, and the use of legal measures such as withholding licenses, etc., to address non-cooperative centers. The most crucial factor in changing attitudes is education. Therefore, it is essential to emphasize the importance of registering and reporting data in universities through dedicated lessons, especially for medical and nursing students, in addition to training on the identification and treatment of infectious diseases.

The next highest mean score/percentage was related to two items with similar scores, two related to dimension “b”: Design of current information systems for registration and reporting: Lack of communication between systems, and using simultaneous record in multiple and separate systems and lack of a single and integrated system from the most peripheral level (health center) to the highest level of the healthcare sector (Ministry of Health) to record and report all infectious disease data. During the COVID-19 epidemic, significant gaps and challenges in information systems have been identified, including the lack of interoperability of systems for seamless data exchange between various organizations in the health system. The COVID-19 epidemic has led to the production of low-quality data due to the existence of various systems for registering and reporting data. As a result, some universities have developed local systems to ensure the accurate registering and reporting of necessary data. The integration and exchange of data between systems, especially during the epidemic of infectious diseases, has become an urgent necessity. This facilitates disease surveillance at higher levels by providing a comprehensive overview of the current situation, conserving resources, and enhancing outcomes. Shanbezadeh and colleagues have also emphasized the significance and vitality of integrated and interactive information systems26. The implementation of such systems can help reduce personnel workload, lower costs, and ultimately mitigate the disease burden during an epidemic by improving access to essential data at the right time. During the COVID-19 pandemic, data registration and reporting were carried out electronically in all health centers. Only hospitals and laboratories conducting diagnostic tests had an electronic registration and reporting system. For this reason, only the mentioned centers were mandated to register and report data, while other facilities such as offices, clinics, imaging centers, and laboratories had a minimal role in registering and reporting infectious diseases. One of the most fundamental measures to control the epidemic of infectious diseases is the establishment of a systematic structure for registering and reporting data from all health service providers 13. By establishing an integrated system and implementing it across all data registering and reporting centers, we can reduce personnel workload, improve data quality, and ensure timely access to recorded data. In fact, it can be said that by designing an integrated system that can be connected to all systems for registering and reporting data and can be used in all centers responsible for registering and reporting data, it can be expected that the current problems of data registering and reporting will be reduced.

Other weaknesses have been also identified, related to legal capacities and tools to deal with non-university sectors in cases of not registering/reporting was the next factor depicted in our study, which requires changes in healthcare policies. Although there are requirements and laws in the discussion of infectious diseases, they are not enough and should be strengthened28. Motivation is the driving force behind work, and the decrease or absence of motivation among employees responsible for registration and reporting is a common issue32. One of the reasons for the decrease in motivation could be attributed to the limited knowledge and awareness of the individuals responsible for registration and reporting, the absence of specific tasks, their heavy workload, and the lack of feedback from higher levels33. In fact, managers and policymakers can enhance people's motivation through detailed planning to carry out actions such as holding training and retraining courses, providing financial incentives for personnel, and leading organizations in registering and reporting data. They can also develop legal tools, such as not granting a license to an office, reducing evaluation and accreditation points, and reducing financial credits, to address institutions that are negligent in performing their duties.

In fact, it can be said that the variation in workflow at each treatment center will lead to numerous challenges when collecting data through local systems. Therefore, it is recommended to establish a global program overseen by central authorities. Until then, higher authorities should consider human factors, resources, and processes involved, and monitor the quality of collected data.

Beside weaknesses, we also evaluated the strengths of this system and the results showed “The structure of the network system in Iran, and the existence of health units at the level of all cities and villages” as the highest score in the first dimension (e) and “Drawing the epidemic curve during an epidemic or outbreak of infectious diseases” as the highest score in the second dimension (f). The establishment of a network-based health service that reaches the smallest and most remote areas of the country ensures equal access for all members of society. This structure is particularly crucial during epidemics of infectious diseases. Such a pattern has been observed in other developing nations. This issue indicates that the problem lies not in the organizational structure, but rather in the functions and policies. HabibiSaravi and Moradi et al.'s findings support this conclusion27,28. The distinction between our study and theirs lies in the fact that we employed the questionnaire method, while they utilized the interview method. Our study focused on evaluating the strengths and weaknesses of the infectious disease data management system, while their study was centered on the strengths and weaknesses of the infectious disease surveillance system. Epidemic curves are a powerful tool for visualizing the progression of a disease and the impact of implemented measures. This tool can be used in conjunction with new tools and software to predict and evaluate future epidemic situations. The studies by Pinto 34, Brum35, and their colleagues are focused in this direction.

Limitation

One of the limitations of the present study was the city lockdown during the peak infection in Ahvaz that made the researcher select the next nearest medical university (Bushehr) for witnessing the process of work and interview the employees. For the same reason, there were few employees present at work and most were working from home and not accessible in-person. The present ones would also reject to cooperate with the researcher, because of their high workload. The online system of data collection also had some issues; some were not familiar with the system and did not save/send the results, which resulted in low response rate. In addition, all our study results are based on the personal perspectives of the participants and were thus subjective. Although in this study, an effort was made to ask as many relevant people as possible, a small part of health system experts participated in this study due to the scope of the research and the large number of people in practice.

Conclusion

The results of the current research indicate that the problem is not in the structure, but in the functions and implementation of the approaches. The most significant challenge in Iran's health information management system during the epidemic of infectious diseases is the insufficient cooperation and participation of public and private centers in registering and reporting data. Addressing this issue is the first step that needs to be taken at the national level. In fact, measures such as training, enacting laws and penalties for failure to record and report data, providing material and spiritual incentives, and integrating all data registering and reporting systems can help generate high-quality and valuable data. This tool can help managers and policymakers make informed decisions to manage and control infectious disease epidemics before they develop into a global disaster.

Data availablity

The collected raw data that support the findings of this study are available from Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran, but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran.

Abbreviations

BSc Bachelor of science

HSE Health system evolution

MCMC Medical care monitoring center

MD Doctor of medicine

MSc Master of science

PhD Doctor of philosophy

SEPAS The project of electronic health record system in Iran is called SEPAS

SIB Integrated health system

Acknowledgements

The authors express their gratitude to the Deputy of Research at Ahvaz Jundishapur University of Medical Sciences for their support in conducting this study. Additionally, the authors extend their sincere appreciation to the staff at Bushehr University of Medical Sciences for their collaboration during the research. Special thanks are also extended to the COVID-19 Registry System in Khuzestan province for their participation in the study.

Author contributions

A.J. and J.Z. contributed to conceptualization, data curation, and writing and editing. S.S. and N.F. contributed to data collection and analysis. All authors writing the draft, reviewed the final version of the manuscript and approved it to submit.

Funding

This study was supported by Ahvaz Jundishapur University of Medical Sciences (Grant No: U-00086).

Competing interests

The authors declare no competing interests.

Ethical approval

This study was approved by the Ethics Committee of Ahvaz Jundishapur University of Medical Sciences (Code: IR.AJUMS.REC.1400.153).

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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References

1. Kagan D Moran-Gilad J Fire M Scientometric trends for coronaviruses and other emerging viral infections GigaScience 2020 9 giaa085 10.1093/gigascience/giaa085 32803225
Kagan, D., Moran-Gilad, J. & Fire, M. Scientometric trends for coronaviruses and other emerging viral infections. GigaScience 9, giaa085 (2020).32803225 10.1093/gigascience/giaa085
2. Reperant LA Osterhaus AD AIDS, Avian flu, SARS, MERS, Ebola, Zika… What next? Vaccine 2017 35 4470 4474 10.1016/j.vaccine.2017.04.082 28633891
Reperant, L. A. & Osterhaus, A. D. AIDS, Avian flu, SARS, MERS, Ebola, Zika… What next?. Vaccine 35, 4470–4474 (2017).28633891 10.1016/j.vaccine.2017.04.082
3. Pakzad B Owlia MB Corona pandemic, earth restart button SSU_J. 2020 28 1 2229 2234
Pakzad, B. & Owlia, M. B. Corona pandemic, earth restart button. SSU_J. 28(1), 2229–2234 (2020).
4. Asif S Alam T Naveed S Interveninig Swine Flu Epidemic J Bioequiv. Avail. 2016 8 194 196
Asif, S., Alam, T. & Naveed, S. Interveninig Swine Flu Epidemic. J Bioequiv. Avail. 8, 194–196 (2016).
5. Roberts JD Tehrani SO Environments, behaviors, and inequalities: reflecting on the impacts of the influenza and coronavirus pandemics in the United States Int. J. Environ. Res. Public Health 2020 17 4484 10.3390/ijerph17124484 32580429
Roberts, J. D. & Tehrani, S. O. Environments, behaviors, and inequalities: reflecting on the impacts of the influenza and coronavirus pandemics in the United States. Int. J. Environ. Res. Public Health 17, 4484 (2020).32580429 10.3390/ijerph17124484
6. Giron S Vector-borne transmission of Zika virus in Europe, southern France, August 2019 Eurosurveillance 2019 24 1900655 10.2807/1560-7917.ES.2019.24.45.1900655 31718742
Giron, S. et al. Vector-borne transmission of Zika virus in Europe, southern France, August 2019. Eurosurveillance 24, 1900655 (2019).31718742 10.2807/1560-7917.ES.2019.24.45.1900655
7. Tavakoli A A comprehensive review of Zika virus infection J. Inflamm. Dis. 2018 22 87 105
Tavakoli, A. et al. A comprehensive review of Zika virus infection. J. Inflamm. Dis. 22, 87–105 (2018).
8. Gubler DJ Vasilakis N Musso D History and emergence of Zika virus J. Infect. Dis. 2017 216 S860 S867 10.1093/infdis/jix451 29267917
Gubler, D. J., Vasilakis, N. & Musso, D. History and emergence of Zika virus. J. Infect. Dis. 216, S860–S867 (2017).29267917 10.1093/infdis/jix451
9. Khoobdel M Jonaidi N Izadi M Does the Zika arbovirus threaten Iran and other countries in the Middle East region? Military Med. 2022 17 187 190
Khoobdel, M., Jonaidi, N. & Izadi, M. Does the Zika arbovirus threaten Iran and other countries in the Middle East region?. Military Med. 17, 187–190 (2022).
10. Soltannezhad, F. Zika Virus. (2017) Military Caring Sci. 3, 272–278. 10.18869/acadpub.mcs.3.4.272
11. Masoumi-Asl H, Kolifarhood G, Gouya MM. The epidemiology of cholera in the Islamic Republic of Iran, 1965-2014. East. Mediterranean Health J. 2020;26(9).
12. Chaleplioglou, A. & Kyriaki-Manessi, D. Comparison of Citations Trends between the COVID-19 Pandemic and SARS-CoV, MERS-CoV, Ebola, Zika, Avian and Swine Influenza Epidemics. arXiv preprint arXiv:2006.05366 (2020).
13. Haghiri H Rabiei R Hosseini A Moghaddasi H Asadi F Notifiable diseases surveillance system with a data architecture approach: a systematic review Acta Inf. Med. 2019 27 268 10.5455/aim.2019.27.268-277
Haghiri, H., Rabiei, R., Hosseini, A., Moghaddasi, H. & Asadi, F. Notifiable diseases surveillance system with a data architecture approach: a systematic review. Acta Inf. Med. 27, 268 (2019).10.5455/aim.2019.27.268-277
14. Shanbehzadeh M Design and Implementation of COVID-19 information management system and registry J. Title 2020 3 0
Shanbehzadeh, M. Design and Implementation of COVID-19 information management system and registry. J. Title 3, 0 (2020).
15. Sabbaghinejad Z Heidari G 15 Definitions of Information Management (IM) J. Studies Library Inf. Sci. 2016 7 39 58
Sabbaghinejad, Z. & Heidari, G. 15 Definitions of Information Management (IM). J. Studies Library Inf. Sci. 7, 39–58 (2016).
16. Kebede M Adeba E Chego M Evaluation of quality and use of health management information system in primary health care units of east Wollega zone, Oromia regional state, Ethiopia BMC Med. Inf. Decision Making 2020 20 1 12
Kebede, M., Adeba, E. & Chego, M. Evaluation of quality and use of health management information system in primary health care units of east Wollega zone, Oromia regional state, Ethiopia. BMC Med. Inf. Decision Making 20, 1–12 (2020).
17. Mambile, C. & Mwogosi, A. Bridging the Gap: A Comprehensive Evaluation of the Government of Tanzania Hospital Management Information System (GoTHOMIS) through Participatory Action Research. (2023).
18. Sadoughi F Nasiri S Langharizadeh M A model for perinatal information management system in Iran Payesh 2015 14 167 179
Sadoughi, F., Nasiri, S. & Langharizadeh, M. A model for perinatal information management system in Iran. Payesh 14, 167–179 (2015).
19. Ajami, S. & Hosseini, M. A comparative study on the features of Iran Health Population Information Management System with the United Nations standards. Journal of Health Administration (JHA) 15 (2013).
20. Yazdizadeh B Sajadi HS Mohtasham F Mohseni M Majdzadeh R Systematic review and policy dialogue to determine challenges in evidence-informed health policy-making: findings of the SASHA study Health Res. Policy Syst. 2021 19 1 13 10.1186/s12961-021-00717-x 33388085
Yazdizadeh, B., Sajadi, H. S., Mohtasham, F., Mohseni, M. & Majdzadeh, R. Systematic review and policy dialogue to determine challenges in evidence-informed health policy-making: findings of the SASHA study. Health Res. Policy Syst. 19, 1–13 (2021).33388085 10.1186/s12961-021-00717-x
21. Holmdahl I Buckee C Wrong but useful—what covid-19 epidemiologic models can and cannot tell us N Engl. J. Med. 2020 383 303 305 10.1056/NEJMp2016822 32412711
Holmdahl, I. & Buckee, C. Wrong but useful—what covid-19 epidemiologic models can and cannot tell us. N Engl. J. Med. 383, 303–305 (2020).32412711 10.1056/NEJMp2016822
22. Gouya M-M Seif-Farahi K Hemmati P An overview of Iran's actions in response to the COVID-19 pandemic and in building health system resilience Front. Public Health 2023 11 1073259 10.3389/fpubh.2023.1073259 36817898
Gouya, M.-M., Seif-Farahi, K. & Hemmati, P. An overview of Iran’s actions in response to the COVID-19 pandemic and in building health system resilience. Front. Public Health 11, 1073259 (2023).36817898 10.3389/fpubh.2023.1073259
23. Zarei J A study to design minimum data set of COVID-19 registry system BMC Infect. Dis. 2021 21 1 13 10.1186/s12879-021-06507-8 33390160
Zarei, J. et al. A study to design minimum data set of COVID-19 registry system. BMC Infect. Dis. 21, 1–13 (2021).33390160 10.1186/s12879-021-06507-8
24. Sheikhtaheri A Tabatabaee Jabali SM Bitaraf E TehraniYazdi A Kabir A A near real-time electronic health record-based COVID-19 surveillance system: an experience from a developing country Health Inf. Manage. J. 2024 53 2 145 154
Sheikhtaheri, A., Tabatabaee Jabali, S. M., Bitaraf, E., TehraniYazdi, A. & Kabir, A. A near real-time electronic health record-based COVID-19 surveillance system: an experience from a developing country. Health Inf. Manage. J. 53(2), 145–154 (2024).
25. Negro-Calduch E Azzopardi-Muscat N Nitzan D Pebody R Jorgensen P Novillo-Ortiz D Health information systems in the COVID-19 pandemic: a short survey of experiences and lessons learned from the European region Front. Public Health 2021 9 676838 10.3389/fpubh.2021.676838 34650946
Negro-Calduch, E. et al. Health information systems in the COVID-19 pandemic: a short survey of experiences and lessons learned from the European region. Front. Public Health 9, 676838 (2021).34650946 10.3389/fpubh.2021.676838
26. Shanbehzadeh, M., Kazemi, A. H., Nopour, R., Haqiri, H., MOBASHERI, F. & Bazvandnezhad, Z. Data Architecture Of Coronavirus Disease 2019 Surveillance System: A Systematic Review. (2021).
27. Moradi G The communicable diseases surveillance system in iran: challenges and opportunities Arch. Iran Med. 2019 22 361 368 31679378
Moradi, G. et al. The communicable diseases surveillance system in iran: challenges and opportunities. Arch. Iran Med. 22, 361–368 (2019).31679378
28. HabibiSaravi R Khankeh H Azar A Ghasemihamedani F Communicable diseases surveillance system in Iran: Strengths and weaknesses 30 years following its implementation Health Emerg. Dis. Quart. 2019 5 25 36 10.32598/hdq.5.1.34.1
HabibiSaravi, R., Khankeh, H., Azar, A. & Ghasemihamedani, F. Communicable diseases surveillance system in Iran: Strengths and weaknesses 30 years following its implementation. Health Emerg. Dis. Quart. 5, 25–36 (2019).10.32598/hdq.5.1.34.1
29. Kazerooni PA Fararouei M Nejat M Akbarpoor M Sedaghat Z Under-ascertainment, under-reporting and timeliness of Iranian communicable disease surveillance system for zoonotic diseases Public Health 2018 154 130 135 10.1016/j.puhe.2017.10.029 29241098
Kazerooni, P. A., Fararouei, M., Nejat, M., Akbarpoor, M. & Sedaghat, Z. Under-ascertainment, under-reporting and timeliness of Iranian communicable disease surveillance system for zoonotic diseases. Public Health 154, 130–135 (2018).29241098 10.1016/j.puhe.2017.10.029
30. Zarei J Dastoorpoor M Jamshidnezhad A Cheraghi M Sheikhtaheri A Regional COVID-19 registry in Khuzestan, Iran: A study protocol and lessons learned from a pilot implementation Inf. Med. Unlocked 2021 23 100520 10.1016/j.imu.2021.100520
Zarei, J., Dastoorpoor, M., Jamshidnezhad, A., Cheraghi, M. & Sheikhtaheri, A. Regional COVID-19 registry in Khuzestan, Iran: A study protocol and lessons learned from a pilot implementation. Inf. Med. Unlocked 23, 100520. 10.1016/j.imu.2021.100520 (2021).10.1016/j.imu.2021.100520
31. Baygi MZ Seyedin H Imbalance between goals and organizational structure in primary health care in Iran-a systematic review Iran. J. Public Health 2013 42 665 24427745
Baygi, M. Z. & Seyedin, H. Imbalance between goals and organizational structure in primary health care in Iran-a systematic review. Iran. J. Public Health 42, 665 (2013).24427745
32. Moradi, A., Erfani, H., Zanganeh, M., Neshani, A., Alipour AliMohammad, R. & Mostafavi, E. Evaluation of the cooperation of private physicians in the notifiable disease reporting in Hamadan province. (2015).
33. Janati A Hosseiny M Gouya MM Moradi G Ghaderi E Communicable disease reporting systems in the world: a systematic review article Iran. J. Public Health 2015 44 1453 26744702
Janati, A., Hosseiny, M., Gouya, M. M., Moradi, G. & Ghaderi, E. Communicable disease reporting systems in the world: a systematic review article. Iran. J. Public Health 44, 1453 (2015).26744702
34. Pinto AD Rodrigues CA Nascimento CL Cruz LA Santos EG Nunes PC Costa MG Rocha MO Covid-19 epidemic curve in Brazil: A sum of multiple epidemics, whose inequality and population density in the states are correlated with growth rate and daily acceleration. An ecological Study Rev. Soc. Brasil. Med. Trop. 2022 25 55 0118 2021
Pinto, A. D. et al. Covid-19 epidemic curve in Brazil: A sum of multiple epidemics, whose inequality and population density in the states are correlated with growth rate and daily acceleration. An ecological Study. Rev. Soc. Brasil. Med. Trop. 25(55), 0118–2021 (2022).
35. Brum AA Duarte-Filho GC Ospina R Almeida FA Macêdo AM Vasconcelos GL ModInterv: an automated online software for modeling epidemics Software Impacts 2022 14 100409 10.1016/j.simpa.2022.100409 35990010
Brum, A. A. et al. ModInterv: an automated online software for modeling epidemics. Software Impacts 14, 100409 (2022).35990010 10.1016/j.simpa.2022.100409
