Trend of Zoonotic Diseases Outbreaks in India: a secondary data analysis of Integrated Disease Surveillance Programme data, 2010- 2023

Trend of Zoonotic Diseases Outbreaks in India: a secondary data analysis of Integrated Disease Surveillance Programme data, 2010- 2023 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Trend of Zoonotic Diseases Outbreaks in India: a secondary data analysis of Integrated Disease Surveillance Programme data, 2010- 2023 Rama Shankar Rath, Rizwan S.A., Pradip Kharya, Aroop Mohanty, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6086195/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Zoonotic diseases are occurring more frequently in different parts of the world. In country like India many of the outbreaks are being reported by the Integrated Diseases Surveillance Program (IDSP). The objective of the study is to find the trend of zoonotic diseases as reported in the IDSP portal. Methods: Data was collected from the IDSP portal from January 2010 to December 2023. Data were collected regarding the outbreak's start date, the number of cases, and the number of deaths. A total ten diseases were included in the study. The data was entered in MS Excel and analysis was done using Stata-12 and MS Excel. Results: From all the reported outbreaks a total of 1158 outbreaks were reported of the selected zoonotic diseases. JE/ AES was the most commonly reported outbreak. Anthrax, JE/ AES, CCHF, Leptospirosis, and Scrub outbreaks and cases were reported throughout the year. The majority of diseases were reported in the rainy season in the months July to September. Conclusion: Although an increasing trend of the selected zoonotic diseases is reported in the current study, the data must be interpreted cautiously keeping in mind the limitations of reporting. Activities may be planned based on burden estimation to prevent the morbidity and mortality associated with the diseases. Zoonoses Zoonosis India Trend IDSP Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Zoonotic diseases are defined as diseases that are transmitted to man from a vertebrate host. There is a wide range of diseases covered in the Zoonotic diseases category. [ 1 ] According to the Centre for Disease Control, USA, around 60% of infectious diseases and 75% of emerging infectious diseases are spread to humans from animals. [ 2 ] In the last two decades, frequent outbreaks of zoonotic diseases like H1N1, H5N1, Ebola, Marburg virus, etc. have occurred in different parts of the world. [ 3 – 5 ] Various factors related to the agent, host, and environment (both micro and macro) affect the disease pattern in different geographic regions. [ 6 ] Environmental factors are one of the important aspects that shows the appropriate completion of the epidemiological triangle. [ 7 ] Many studies have demonstrated the seasonal and temporal variation in the trend of zoonotic diseases in different parts of the world. [ 8 ] Studying the pattern of disease outbreaks in the past guides us to take preventive measures to limit the outbreaks and also prepare to manage the cases in the future. Integrated Disease Surveillance Programme (IDSP) is one of the key programme for reporting disease outbreaks throughout the country. The data from a very granular level is compiled from each reporting unit and reported to the district level and from there, it is compiled at the state and national levels. [ 9 ] The data is published in the IDSP portal with a lag period of almost three weeks. The availability of data gives us an opportunity to identify the pattern and take preventive measures according to the distribution of the disease. The study’s main objective is to assess the decadal trend of zoonotic diseases in India as reported by the Integrated Disease Surveillance Program from January 2010 to December 2023. Methodology This study is a secondary data analysis of outbreaks reported by the IDSP in the weekly reports of outbreaks on the website. [ 9 ] Data were collected regarding the outbreak's start date and number of cases. The data was updated taking into account the progress of the outbreak as the reports published in the subsequent reports of the outbreak. Taking the lag period of reporting outbreaks from three weeks or more. The weekly reports for those weeks which were not available at the site were excluded from the analysis. All the diseases which were described as zoonotic disease under IDSP were included in the analysis. All the outbreaks which were reported late were also included in the analysis. States like Telangana and Andhra Pradesh, the outbreaks were considered as same as that of undivided Andhra Pradesh. Data Extraction and Analysis: The data of all the weekly reports published in the study period were included. The data were entered in Microsoft Excel and analysis was done using Microsoft Excel and Stata-12. The number of outbreaks and cases reported were represented in the Indian political map as a heat map. Binomial Poisson regression was used, and model fit was assessed using the Akaike information criterion. Incidence risk ratio was calculated to find the annual increase in the number of cases for disease reporting. Since this is a secondary data analysis of all the weekly reports published in the open platform, no ethical approval was taken. Results Disease-wise outbreaks: From all the reported outbreaks a total of 1158 outbreaks were reported of the selected zoonotic diseases. The majority of the outbreaks were reported from JE/ AES (35.1%) followed by Leptospirosis (17.1%), Scrub typhus (12.3%), Anthrax (9.7%), and followed by CCHF (7.3%). The rest of the diseases contribute to approximately 18.4% of outbreaks. A detailed description of the number of cases and outbreaks is represented in Fig. 1. Trend of Zoonotic Diseases: The total number of outbreaks increased from 33 outbreaks in 2010 to almost 166 outbreaks in 2019. (Figure-2) On using Poisson regression, we found that the outbreaks of the selected diseases are increasing by 8.2% (CI: 6.6–9.8) annually. Of all diseases reported annually, JE, Scrub typhus, CCHF showed a statistically significant increasing trend of outbreaks over the decade. A detailed comparison among different for all the diseases reporting nonzero cases is provided in table 1. If we compare the number of outbreaks reported of the selected outbreaks from 2010 to 2019 and 2020 to 2023 there is significant 26% annual increase in the cases from 2020 to 2023 whereas the increase in cases from 2010 to 2013 is 12%. Seasonality of Zoonotic Diseases: Outbreaks and cases of Anthrax, JE/ AES were mostly reported in April, May and June. KFD, Scrub, Leptospirosis, CEV, and CCHF cases occur in June July, and August i.e., the rainy season in many parts of India. Outbreaks of Influenza A and its subtypes mainly occurred in the January and February months which are typically winter season in India. The detailed month wise distribution of the diseases is reported in figure-3. Geographic Distribution of Outbreaks: The geographic distribution of zoonotic diseases varied from disease to disease. The majority of the outbreaks were reported from states like Gujrat, Assam, Tamil Nadu, Odisha and Maharashtra. Very few cases were reported from northern and central states. (Figure-4). Scrub typhus and JE were reported from all over India. Disease like Anthrax has been reported primarily from the costal states in the eastern and the southern states, whereas Leptospirosis and KFD were reported primarily from the western and southern states. CCHF was primarily reported from Gujurat and Rajasthan. Discussion This was a secondary data analysis to assess the overall burden of Zoonotic diseases and their trend in the last decade. In this study, we found most of the cases and outbreaks were reported from the JE/ AES, Scrub typhus, Anthrax, leptospirosis and only. Most of the selected diseases have a seasonal distribution with the majority of outbreaks occurring in the rainy season except a few like Anthrax, which occurs primarily in summer. Overall diseases like JE, Scrub typhus, CCHF and Leptospirosis have been shown to have an increasing trend. The increase in number of outbreaks over the years at a rate of 8.2%, with decreasing number of cases shows increased geographic spread or increasing circulation of infectious agents among the susceptible population which resulted in the frequent outbreaks. Similar increasing trends of the outbreaks were reported by many studies conducted worldwide. [ 10 – 14 ] The increased number of outbreaks may be attributed to patient level factors like increased awareness and health system factors like, improved surveillance, increased access to health system and enhanced laboratory capacity both in terms of quantity and penetration in the health system over the study period. Apart from this the increase in cases from 2020 to 2023 has increased at a rate of 26% i.e. more than twice as high as reported from 2010 to 2019. This might be due to the decreased reporting in the COVID-19 period and recovery of the reporting is returning to normalcy post-COVID which is evident from the dip in the number of reported outbreaks in the 2020, 2021 and 2022. Over the years the selected diseases showed an increasing geographical coverage which might be attributed due to migration both within and between states, increased human animal interaction. Rapid transportation facility like aviation which increased significantly over the study period might have contributed to rapid transport of the infected individuals within the incubation period. Most zoonotic diseases were reported in the month of May to October in the study decade except Anthrax which is reported mostly in summer seasons. This is like that reported by other studies. [ 15 – 18 ] Very few outbreaks were reported from populus states like Rajasthan, Madhya Pradesh, Uttar Pradesh, Bihar and Jharkhand which raises query regarding the quality of surveillance in these states. [ 19 ] The study is currently limited by the fact that the data is collected from the IDSP portal only and thus may or may not represent the actual number of disease outbreaks and the number of cases in the country. Secondly, the diagnosis of many diseases is only presumptive or epidemiological, but not all cases are laboratory confirmed. Thirdly the cases reported to the private sector are not reported in the IDSP, whereas private care providers cater to almost 40% of inpatient care, and outpatient care varies from as low as 9% to as high as 78%. [ 20 ] Thus the data must be interpreted cautiously. References Zoonosis WH, Organization Available at: https://www.who.int/news-room/fact-sheets/detail/zoonoses Accessed on 12/02/25 Zoonotic, Diseases Centre for Disease Control, Atlanta, USA available at: https://www.cdc.gov/one-health/about/about-zoonotic-diseases.html?CDC_AAref_Val=https://www.cdc.gov/onehealth/basics/zoonotic-diseases.html , Accessed on 12/02/25 Zhong NS, Zheng BJ, Li YM, Poon, Xie ZH, Chan KH, Li PH, Tan SY, Chang Q, Xie JP, Liu XQ, Xu J, Li DX, Yuen KY, Peiris, Guan Y (2003) Epidemiology and cause of severe acute respiratory syndrome (SARS) in Guangdong, People's Republic of China, in February 2003. Lancet 362(9393):1353–1358. 10.1016/s0140-6736(03)14630-2 Zumla A, Hui DS, Perlman S (2015) Middle East respiratory syndrome. Lancet 386(9997):995–1007. 10.1016/S0140-6736(15)60454-8 Epub 2015 Jun 3 Coltart CE, Lindsey B, Ghinai I, Johnson AM, Heymann DL (2017) The Ebola outbreak, 2013–2016: old lessons for new epidemics. Philos Trans R Soc Lond B Biol Sci 372(1721):20160297. 10.1098/rstb.2016.0297 Allen T, Murray KA, Zambrana-Torrelio C, Morse SS, Rondinini C, Di Marco M, Breit N, Olival KJ, Daszak P (2017) Global hotspots and correlates of emerging zoonotic diseases. Nat Commun 8(1):1124. 10.1038/s41467-017-00923-8 European Food Safety Authority The Community Summary Report on trends and sources of zoonoses, zoonotic agents and food-borne outbreaks in the European Union in 2008. Available at: http://www.efsa.europa.eu/en/scdocs/doc/1496.pdf , Assessed om 12/02/2025 Mapping the epidemiological distribution (2019) and incidence of major zoonotic diseases in South Tigray, North Wollo and Ab'ala (Afar), Ethiopia. PLoS ONE 14(1):e0211292. 10.1371/journal.pone.0211292 Integrated Disease Surveillance Project, Government of India available at: https://idsp.nic.in/index4.php?lang=1&level=0&linkid=412&lid=3695 , Accessed on: 12/02/2025 Jones K, Patel N, Levy M, Storeygard A, Balk D, Gittleman JL, Daszac P (2008) Global trends in emerging infectious diseases. Nature 451:990–993 Stephens PR, Gottdenker N, Schatz AM, Schmidt JP, Drake JM (2021) Characteristics of the 100 largest modern zoonotic disease outbreaks. Phil Trans R Soc B 376:20200535 Meadows AJ, Stephenson N, Madhav NK, Oppenheim B (2023) Historical trends demonstrate a pattern of increasingly frequent and severe spillover events of high-consequence zoonotic viruses. BMJ Global Health 8:e012026 Lee HW, Cho PY, Moon SU et al (2015) Current situation of scrub typhus in South Korea from 2001–2013. Parasites Vectors 8:238 Spengler JR, Bente DA, Bray M, Burt F, Hewson R, Korukluoglu G, Mirazimi A, Weber F, Papa A (2018) Second International Conference on Crimean-Congo Hemorrhagic Fever. Antiviral Res. ;150:137–147. 10.1016/j.antiviral.2017.11.019 Lal A, Hales S, French N, Baker MG (2012) Seasonality in Human Zoonotic Enteric Diseases: A Systematic Review. PLoS ONE 7(4):e31883 Narang R, Deshmukh P, Jain J, Jain M, Raut A, Deotale V, Pote K, Rahi M (2022) Scrub typhus in urban areas of Wardha district in central India. Indian J Med Res 156(3):435–441. 10.4103/ijmr.IJMR_707_19 Munang'andu HM, Banda F, Siamudaala VM, Munyeme M, Kasanga CJ, Hamududu B (2012) The effect of seasonal variation on anthrax epidemiology in the upper Zambezi floodplain of western Zambia. J Vet Sci 13(3):293–298. 10.4142/jvs.2012.13.3.293 Singh P, Kumar P, Dhiman RC Kyasanur forest disease and climatic attributes in India. J Vector Borne Dis 2022 Jan-Mar ;59(1):79–85. 10.4103/0972-9062.331408 Mahapatra S, Kumar A, Jha R, Paul S Physicians Practices related to the disease surveillance activities under the integrated disease surveillance programme in Bihar India, The center for Health Policy, Asian Development Research Institute, India, Available at: https://www.adriindia.org/images/paper/15977475502.pdf , Accessed on 12/02/25 Jain N, Kumar A, Nandraj S, Furtado KM NSSO 71st round Same data Multiple Interpretations, Economic and Political Weekly, 46–47, 2015 Available at: https://www.niti.gov.in/sites/default/files/2019-01/NSSO_71st_Round%20_Final.pdf Accessed on 12/02/25 Tables Table-1: Poisson Regression analysis for different diseases with non-zero values Disease outbreaks IRR P value AIC BIC JE/ AES 1.04 (1.01- 1.06) 0.002 151.92 153.20 Leptospirosis 1.09 (1.06- 1.13) 0.000 87.89 89.16 Scrub Typhus 1.15 (1.10- 1.21) 0.000 78.97 80.25 All diseases 1.08 (1.07- 1.10) 0.000 167.43 168.71 All diseases (2010-2019) 1.12 (1.09-1.15) 0.000 99.14 99.74 All diseases (2020- 2023) 1.26 (1.16-1.38) 0.000 45.03 43.81 IRR: Incidence Rate Ratio Additional Declarations The authors declare no competing interests. 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There is a wide range of diseases covered in the Zoonotic diseases category. [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] According to the Centre for Disease Control, USA, around 60% of infectious diseases and 75% of emerging infectious diseases are spread to humans from animals. [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] In the last two decades, frequent outbreaks of zoonotic diseases like H1N1, H5N1, Ebola, Marburg virus, etc. have occurred in different parts of the world. [\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] Various factors related to the agent, host, and environment (both micro and macro) affect the disease pattern in different geographic regions. [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] Environmental factors are one of the important aspects that shows the appropriate completion of the epidemiological triangle. [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] Many studies have demonstrated the seasonal and temporal variation in the trend of zoonotic diseases in different parts of the world. [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] Studying the pattern of disease outbreaks in the past guides us to take preventive measures to limit the outbreaks and also prepare to manage the cases in the future.\u003c/p\u003e \u003cp\u003eIntegrated Disease Surveillance Programme (IDSP) is one of the key programme for reporting disease outbreaks throughout the country. The data from a very granular level is compiled from each reporting unit and reported to the district level and from there, it is compiled at the state and national levels. [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] The data is published in the IDSP portal with a lag period of almost three weeks. The availability of data gives us an opportunity to identify the pattern and take preventive measures according to the distribution of the disease. The study\u0026rsquo;s main objective is to assess the decadal trend of zoonotic diseases in India as reported by the Integrated Disease Surveillance Program from January 2010 to December 2023.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cp\u003eThis study is a secondary data analysis of outbreaks reported by the IDSP in the weekly reports of outbreaks on the website. [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] Data were collected regarding the outbreak's start date and number of cases. The data was updated taking into account the progress of the outbreak as the reports published in the subsequent reports of the outbreak. Taking the lag period of reporting outbreaks from three weeks or more. The weekly reports for those weeks which were not available at the site were excluded from the analysis. All the diseases which were described as zoonotic disease under IDSP were included in the analysis. All the outbreaks which were reported late were also included in the analysis. States like Telangana and Andhra Pradesh, the outbreaks were considered as same as that of undivided Andhra Pradesh.\u003c/p\u003e \u003cp\u003eData Extraction and Analysis: The data of all the weekly reports published in the study period were included. The data were entered in Microsoft Excel and analysis was done using Microsoft Excel and Stata-12. The number of outbreaks and cases reported were represented in the Indian political map as a heat map. Binomial Poisson regression was used, and model fit was assessed using the Akaike information criterion. Incidence risk ratio was calculated to find the annual increase in the number of cases for disease reporting. Since this is a secondary data analysis of all the weekly reports published in the open platform, no ethical approval was taken.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eDisease-wise outbreaks:\u003c/h2\u003e \u003cp\u003eFrom all the reported outbreaks a total of 1158 outbreaks were reported of the selected zoonotic diseases. The majority of the outbreaks were reported from JE/ AES (35.1%) followed by Leptospirosis (17.1%), Scrub typhus (12.3%), Anthrax (9.7%), and followed by CCHF (7.3%). The rest of the diseases contribute to approximately 18.4% of outbreaks. A detailed description of the number of cases and outbreaks is represented in Fig.\u0026nbsp;1.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eTrend of Zoonotic Diseases:\u003c/h3\u003e\n\u003cp\u003eThe total number of outbreaks increased from 33 outbreaks in 2010 to almost 166 outbreaks in 2019. (Figure-2) On using Poisson regression, we found that the outbreaks of the selected diseases are increasing by 8.2% (CI: 6.6\u0026ndash;9.8) annually. Of all diseases reported annually, JE, Scrub typhus, CCHF showed a statistically significant increasing trend of outbreaks over the decade. A detailed comparison among different for all the diseases reporting nonzero cases is provided in table 1. If we compare the number of outbreaks reported of the selected outbreaks from 2010 to 2019 and 2020 to 2023 there is significant 26% annual increase in the cases from 2020 to 2023 whereas the increase in cases from 2010 to 2013 is 12%.\u003c/p\u003e\n\u003ch3\u003eSeasonality of Zoonotic Diseases:\u003c/h3\u003e\n\u003cp\u003eOutbreaks and cases of Anthrax, JE/ AES were mostly reported in April, May and June. KFD, Scrub, Leptospirosis, CEV, and CCHF cases occur in June July, and August i.e., the rainy season in many parts of India. Outbreaks of Influenza A and its subtypes mainly occurred in the January and February months which are typically winter season in India. The detailed month wise distribution of the diseases is reported in figure-3.\u003c/p\u003e\n\u003ch3\u003eGeographic Distribution of Outbreaks:\u003c/h3\u003e\n\u003cp\u003eThe geographic distribution of zoonotic diseases varied from disease to disease. The majority of the outbreaks were reported from states like Gujrat, Assam, Tamil Nadu, Odisha and Maharashtra. Very few cases were reported from northern and central states. (Figure-4).\u003c/p\u003e \u003cp\u003eScrub typhus and JE were reported from all over India. Disease like Anthrax has been reported primarily from the costal states in the eastern and the southern states, whereas Leptospirosis and KFD were reported primarily from the western and southern states. CCHF was primarily reported from Gujurat and Rajasthan.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis was a secondary data analysis to assess the overall burden of Zoonotic diseases and their trend in the last decade. In this study, we found most of the cases and outbreaks were reported from the JE/ AES, Scrub typhus, Anthrax, leptospirosis and only. Most of the selected diseases have a seasonal distribution with the majority of outbreaks occurring in the rainy season except a few like Anthrax, which occurs primarily in summer. Overall diseases like JE, Scrub typhus, CCHF and Leptospirosis have been shown to have an increasing trend.\u003c/p\u003e \u003cp\u003eThe increase in number of outbreaks over the years at a rate of 8.2%, with decreasing number of cases shows increased geographic spread or increasing circulation of infectious agents among the susceptible population which resulted in the frequent outbreaks. Similar increasing trends of the outbreaks were reported by many studies conducted worldwide. [\u003cspan additionalcitationids=\"CR11 CR12 CR13\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] The increased number of outbreaks may be attributed to patient level factors like increased awareness and health system factors like, improved surveillance, increased access to health system and enhanced laboratory capacity both in terms of quantity and penetration in the health system over the study period. Apart from this the increase in cases from 2020 to 2023 has increased at a rate of 26% i.e. more than twice as high as reported from 2010 to 2019. This might be due to the decreased reporting in the COVID-19 period and recovery of the reporting is returning to normalcy post-COVID which is evident from the dip in the number of reported outbreaks in the 2020, 2021 and 2022.\u003c/p\u003e \u003cp\u003eOver the years the selected diseases showed an increasing geographical coverage which might be attributed due to migration both within and between states, increased human animal interaction. Rapid transportation facility like aviation which increased significantly over the study period might have contributed to rapid transport of the infected individuals within the incubation period. Most zoonotic diseases were reported in the month of May to October in the study decade except Anthrax which is reported mostly in summer seasons. This is like that reported by other studies. [\u003cspan additionalcitationids=\"CR16 CR17\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] Very few outbreaks were reported from populus states like Rajasthan, Madhya Pradesh, Uttar Pradesh, Bihar and Jharkhand which raises query regarding the quality of surveillance in these states. [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eThe study is currently limited by the fact that the data is collected from the IDSP portal only and thus may or may not represent the actual number of disease outbreaks and the number of cases in the country. Secondly, the diagnosis of many diseases is only presumptive or epidemiological, but not all cases are laboratory confirmed. Thirdly the cases reported to the private sector are not reported in the IDSP, whereas private care providers cater to almost 40% of inpatient care, and outpatient care varies from as low as 9% to as high as 78%. [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] Thus the data must be interpreted cautiously.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eZoonosis WH, Organization Available at: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.who.int/news-room/fact-sheets/detail/zoonoses\u003c/span\u003e\u003cspan address=\"https://www.who.int/news-room/fact-sheets/detail/zoonoses\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e Accessed on 12/02/25\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZoonotic, Diseases Centre for Disease Control, Atlanta, USA available at: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cdc.gov/one-health/about/about-zoonotic-diseases.html?CDC_AAref_Val=https://www.cdc.gov/onehealth/basics/zoonotic-diseases.html\u003c/span\u003e\u003cspan address=\"https://www.cdc.gov/one-health/about/about-zoonotic-diseases.html?CDC_AAref_Val=https://www.cdc.gov/onehealth/basics/zoonotic-diseases.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, Accessed on 12/02/25\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhong NS, Zheng BJ, Li YM, Poon, Xie ZH, Chan KH, Li PH, Tan SY, Chang Q, Xie JP, Liu XQ, Xu J, Li DX, Yuen KY, Peiris, Guan Y (2003) Epidemiology and cause of severe acute respiratory syndrome (SARS) in Guangdong, People's Republic of China, in February 2003. 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Nature 451:990\u0026ndash;993\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStephens PR, Gottdenker N, Schatz AM, Schmidt JP, Drake JM (2021) Characteristics of the 100 largest modern zoonotic disease outbreaks. Phil Trans R Soc B 376:20200535\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMeadows AJ, Stephenson N, Madhav NK, Oppenheim B (2023) Historical trends demonstrate a pattern of increasingly frequent and severe spillover events of high-consequence zoonotic viruses. BMJ Global Health 8:e012026\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee HW, Cho PY, Moon SU et al (2015) Current situation of scrub typhus in South Korea from 2001\u0026ndash;2013. Parasites Vectors 8:238\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSpengler JR, Bente DA, Bray M, Burt F, Hewson R, Korukluoglu G, Mirazimi A, Weber F, Papa A (2018) Second International Conference on Crimean-Congo Hemorrhagic Fever. Antiviral Res. ;150:137\u0026ndash;147. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.antiviral.2017.11.019\u003c/span\u003e\u003cspan address=\"10.1016/j.antiviral.2017.11.019\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLal A, Hales S, French N, Baker MG (2012) Seasonality in Human Zoonotic Enteric Diseases: A Systematic Review. PLoS ONE 7(4):e31883\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNarang R, Deshmukh P, Jain J, Jain M, Raut A, Deotale V, Pote K, Rahi M (2022) Scrub typhus in urban areas of Wardha district in central India. Indian J Med Res 156(3):435\u0026ndash;441. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.4103/ijmr.IJMR_707_19\u003c/span\u003e\u003cspan address=\"10.4103/ijmr.IJMR_707_19\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMunang'andu HM, Banda F, Siamudaala VM, Munyeme M, Kasanga CJ, Hamududu B (2012) The effect of seasonal variation on anthrax epidemiology in the upper Zambezi floodplain of western Zambia. J Vet Sci 13(3):293\u0026ndash;298. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.4142/jvs.2012.13.3.293\u003c/span\u003e\u003cspan address=\"10.4142/jvs.2012.13.3.293\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSingh P, Kumar P, Dhiman RC Kyasanur forest disease and climatic attributes in India. J Vector Borne Dis 2022 Jan-Mar ;59(1):79\u0026ndash;85. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.4103/0972-9062.331408\u003c/span\u003e\u003cspan address=\"10.4103/0972-9062.331408\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMahapatra S, Kumar A, Jha R, Paul S Physicians Practices related to the disease surveillance activities under the integrated disease surveillance programme in Bihar India, The center for Health Policy, Asian Development Research Institute, India, Available at: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.adriindia.org/images/paper/15977475502.pdf\u003c/span\u003e\u003cspan address=\"https://www.adriindia.org/images/paper/15977475502.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, Accessed on 12/02/25\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJain N, Kumar A, Nandraj S, Furtado KM NSSO 71st round Same data Multiple Interpretations, Economic and Political Weekly, 46\u0026ndash;47, 2015 Available at: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.niti.gov.in/sites/default/files/2019-01/NSSO_71st_Round%20_Final.pdf\u003c/span\u003e\u003cspan address=\"https://www.niti.gov.in/sites/default/files/2019-01/NSSO_71st_Round%20_Final.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e Accessed on 12/02/25\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable-1: Poisson Regression analysis for different diseases with non-zero values\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 27.9605%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDisease outbreaks\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.7105%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIRR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.8158%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2566%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAIC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2566%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBIC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 27.9605%;\"\u003e\n \u003cp\u003eJE/ AES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.7105%;\"\u003e\n \u003cp\u003e1.04 (1.01- 1.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.8158%;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2566%;\"\u003e\n \u003cp\u003e151.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2566%;\"\u003e\n \u003cp\u003e153.20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 27.9605%;\"\u003e\n \u003cp\u003eLeptospirosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.7105%;\"\u003e\n \u003cp\u003e1.09 (1.06- 1.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.8158%;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2566%;\"\u003e\n \u003cp\u003e87.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2566%;\"\u003e\n \u003cp\u003e89.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 27.9605%;\"\u003e\n \u003cp\u003eScrub Typhus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.7105%;\"\u003e\n \u003cp\u003e1.15 (1.10- 1.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.8158%;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2566%;\"\u003e\n \u003cp\u003e78.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2566%;\"\u003e\n \u003cp\u003e80.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 27.9605%;\"\u003e\n \u003cp\u003eAll diseases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.7105%;\"\u003e\n \u003cp\u003e1.08 (1.07- 1.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.8158%;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2566%;\"\u003e\n \u003cp\u003e167.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2566%;\"\u003e\n \u003cp\u003e168.71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 27.9605%;\"\u003e\n \u003cp\u003eAll diseases (2010-2019)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.7105%;\"\u003e\n \u003cp\u003e1.12 (1.09-1.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.8158%;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2566%;\"\u003e\n \u003cp\u003e99.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2566%;\"\u003e\n \u003cp\u003e99.74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 27.9605%;\"\u003e\n \u003cp\u003eAll diseases (2020- 2023)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.7105%;\"\u003e\n \u003cp\u003e1.26 (1.16-1.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.8158%;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2566%;\"\u003e\n \u003cp\u003e45.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2566%;\"\u003e\n \u003cp\u003e43.81\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eIRR: Incidence Rate Ratio\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Zoonosis, India, Trend, IDSP","lastPublishedDoi":"10.21203/rs.3.rs-6086195/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6086195/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Zoonotic diseases are occurring more frequently in different parts of the world. In country like India many of the outbreaks are being reported by the Integrated Diseases Surveillance Program (IDSP). The objective of the study is to find the trend of zoonotic diseases as reported in the IDSP portal.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e Data was collected from the IDSP portal from January 2010 to December 2023. Data were collected regarding the outbreak's start date, the number of cases, and the number of deaths. A total ten diseases were included in the study. The data was entered in MS Excel and analysis was done using Stata-12 and MS Excel.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e From all the reported outbreaks a total of 1158 outbreaks were reported of the selected zoonotic diseases. JE/ AES was the most commonly reported outbreak. Anthrax, JE/ AES, CCHF, Leptospirosis, and Scrub outbreaks and cases were reported throughout the year. The majority of diseases were reported in the rainy season in the months July to September.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e Although an increasing trend of the selected zoonotic diseases is reported in the current study, the data must be interpreted cautiously keeping in mind the limitations of reporting. Activities may be planned based on burden estimation to prevent the morbidity and mortality associated with the diseases.\u003c/p\u003e","manuscriptTitle":"Trend of Zoonotic Diseases Outbreaks in India: a secondary data analysis of Integrated Disease Surveillance Programme data, 2010- 2023","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-27 08:58:09","doi":"10.21203/rs.3.rs-6086195/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3c6fca76-96ff-4ecd-8b3b-8d1b4b533f92","owner":[],"postedDate":"February 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":44724488,"name":"Zoonoses"}],"tags":[],"updatedAt":"2025-02-27T08:58:09+00:00","versionOfRecord":[],"versionCreatedAt":"2025-02-27 08:58:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6086195","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6086195","identity":"rs-6086195","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}