A recent history of disease outbreaks in Kenya, 2007-2022: Findings from routine surveillance data

A recent history of disease outbreaks in Kenya, 2007-2022: Findings from routine surveillance data | 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 Short Report A recent history of disease outbreaks in Kenya, 2007-2022: Findings from routine surveillance data Farida Geteri, Jeanette Dawa, John Gachohi, Samuel Kadivane, Emmanuel Okunga This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3967744/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 15 Oct, 2024 Read the published version in BMC Research Notes → Version 1 posted 10 You are reading this latest preprint version Abstract Background Africa reports the highest number of outbreaks in the world, accounting for 39% of all outbreaks in 2022. The Integrated Disease Surveillance and Response strategy in Kenya ensures the reporting of outbreaks at the national level. We present a summary of the burden of reported disease outbreaks in Kenya, 2007–2022. Methods We reviewed historical surveillance data, 2007–2022. We summarized the annual caseload and deaths of the reported outbreaks per county. We classified the outbreaks into 3 categories i.e., high, moderate, and low burden. We conducted the Mann-Kendall test to detect trends in the number of outbreaks and counties reporting over time. Results Twenty-three outbreaks were reported. COVID-19, cholera, epidemic malaria, leishmaniasis, and measles were associated with high disease burden. The highest number of outbreaks reported in a single year was 10. Garissa, Nairobi, Nakuru, Wajir, Mandera, and Mombasa, had the majority of the outbreaks and caseload. Conclusion There was an increase in the frequency and magnitude of outbreaks. This highlights the complex public health landscape and the vulnerability of the country to epidemics. The differences in outbreak occurrence among counties necessitate targeted and enhanced preventive, preparedness, and response interventions at the sub-national level to reduce the burden of outbreaks. Priority disease outbreak surveillance burden Kenya Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Disease outbreaks cause significant health concerns globally, however, the occurrence of these outbreaks disproportionately affects world regions. Africa reports the highest number of outbreaks in the world (Torres Munguía et al., 2022), accounting for 39% of all outbreaks in 2022 ( 1 ). In Africa, the common causes of epidemics are Vaccine-Preventable Diseases (VPDs), vector-borne, water-borne, and zoonotic diseases (Mboussou et al., 2019). The incidence and impact of diseases vary over time ( 2 ). Despite a rising trend in non-communicable conditions worldwide, the relative burden of infectious diseases is continually changing. Specific infectious diseases may be more significant at the regional, national, or sub-national level while decreasing in importance globally. These differences are best captured by surveillance. Better awareness of the diseases most commonly affecting an area can lead to more accurate disease occurrence maps informed by local data. Ultimately, this leads to better preparedness and more efficient allocation of resources for prevention and control strategies. Surveillance in Kenya is conducted through the Integrated Disease Surveillance and Response (IDSR) strategy adopted from the World Health Organisation. The strategy is designed to collect health data for multiple diseases and public health events using standardized data collection tools. The IDSR strategy ensures the reporting of priority diseases, conditions, and events to the national level in line with the International Health Regulations (IHR) requirements ( 3 ). We present a summary of the burden of disease outbreaks in Kenya between 2007 and 2022 reported as part of the IDSR strategy. Our findings will provide valuable information for the development of sub-national disease occurrence maps and contribute to more efficient use of resources at the county level to inform public health prevention, preparedness, and response measures. Methods In 2023, we reviewed historical surveillance data held by the Disease Surveillance and Response Unit (DSRU) of the Ministry of Health, Kenya, 2007–2022 using the list of priority diseases (Table 1 ). When a suspected case of an outbreak is detected at the community or health facility level, a Community Health Assistant reports to the surveillance officer, who immediately notifies the Sub-County Disease Surveillance Coordinator (SCDSC). The SCDSC then reports to the County Disease Surveillance Coordinator (CDSC), who informs the national-level DSRU within 24 hours of the initial report. Line listing is initiated as soon as an outbreak is confirmed. An outbreak is confirmed when the number of cases exceeds the predefined action threshold and it is considered over when no new cases are reported within two incubation periods. For epidemic-prone diseases, an outbreak is over when the number of cases declines below the action threshold ( 4 ). Table 1 List of priority diseases adapted from Kenya's integrated disease surveillance and response framework, 2021 Epidemic Prone Diseases Diseases targeted for eradication and elimination Diseases, conditions, and events of Public Health Importance Acute flaccid paralysis (Poliomyelitis) Guinea worm Disease (Dracunculiasis) Acute Malnutrition Anthrax Human African Trypanosomiasis Adverse events following immunization (AEFI) Brucellosis Leprosy Aflatoxicosis Bacterial Meningitis Lymphatic filariasis Animal Bites (including Dog bites, snake bites, wild animals) Chikungunya Malaria* Cancers Cholera Measles Diabetes Mellitus (New cases) Dengue Fever Neonatal tetanus Diarrhoea with dehydration in children < 5 years of age Diarrhoea with blood (Shigella) Onchocerciasis Epilepsy Ebola/Marburg Haemorrhagic Fever Rabies Hepatitis A/B/C/E Leishmaniasis Trachoma Hypertension (New cases) Plague Maternal Death Rift Valley Fever (RVF) Neonatal death Smallpox (Variola) Newly diagnosed HIV infection Typhoid Fever Sexually transmitted infections (Gonorrhoea, Syphilis, Chlamydia, Herpes genitalia) Yellow fever Schistosomiasis Influenza Like Illness Severe Pneumonia in Children under five-years-old Influenza due to a new subtype Soil-transmitted helminths SARS Substance Abuse including Alcohol and other Drugs Severe Acute Respiratory Infections Suicides/ attempted suicides Bacterial Meningitis Trauma (Road traffic injury/ Fatality) Tuberculosis (MDR /XDR) Any public health event of national/regional/international concern (infectious, zoonotic, foodborne, chemical, radio-nuclear, or due to an unknown condition) *For this study, data used was epidemic malaria data collected from the malaria epidemic-prone counties. Seasonal malaria data from endemic counties is reported separately. We summarized the archived data from 2007 to 2022, on the annual number of outbreaks, caseload, and deaths of the outbreaks in each of the 47 counties. We abstracted the data into an electronic spreadsheet. Totals were presented in tables and figures where outbreaks and counties were ranked according to the frequency of outbreak occurrence, cases, and deaths. To classify the diseases into 3 categories of burden i.e., high, moderate, and low burden we obtained the median caseload and deaths per outbreak and used these values to classify each outbreak into one of the 3 categories. High-burden outbreaks had both the caseload and deaths higher than the median. Moderate-burden outbreaks had either the caseload or deaths higher than the median. Low-burden outbreaks had both the caseload and deaths lower than the median. To establish whether there were trends in the number of outbreaks detected and counties reporting over time, we conducted the Mann-Kendall test for trends. Analysis was conducted using Microsoft Excel and R ( 5 ). Results We obtained 457 outbreaks occurrence entries over the 16 years, with most outbreaks occurring severally across the years. Seven( 7 ) entries in 2012, 2013, and 2014 had missing information on the counties affected. Twenty-three outbreaks were reported between 2007 and 2022. More than half, 13(59%) of the diseases were epidemic-prone diseases: COVID-19, cholera, epidemic malaria, leishmaniasis, dengue fever, measles, chikungunya, influenza A, severe acute respiratory illness (SARI), Rift Valley Fever (RVF), anthrax, yellow fever, and salmonella. Seven(32%) of the diseases; hepatitis A, B, and E, pertussis, aflatoxicosis, mumps, and Q-fever were diseases of public health importance. Two(9%) of the diseases were targeted for eradication and elimination: rabies and poliomyelitis including (acute flaccid paralysis [AFP] and vaccine-derived poliovirus 2 [VDPV2]). The reported outbreak alert and action thresholds are listed in Table 2 . Table 2 List of reported disease outbreaks alert and action thresholds, adapted from Kenya's integrated disease surveillance and response framework, 2021 Disease Alert Threshold Action Threshold Acute flaccid paralysis 1 suspected case 1 confirmed case Aflatoxicosis 1 suspected case 1 confirmed case Anthrax 1 suspected case 1 confirmed case Chikungunya 1 suspected case 1 confirmed case Cholera 1 suspected case 1 confirmed case COVID-19 3 confirmed cases within 2 weeks in a previously unaffected area Positivity rate > 5% Dengue fever Cases above 1 standard deviation from the 5-year mean data per geographical area Cases above 2 standard deviations from the 5-year mean data per geographical area Influenza A Cases above 1 standard deviation from the 5-year mean data per geographical area Cases above 2 standard deviations from the 5-year mean data per geographical area Leishmaniasis 1 suspected case 1 confirmed case Malaria Unusual increase in the number of new malaria cases or deaths as compared to the same period in the previous years The number of new cases exceeds the upper limit of cases seen in a previous non-epidemic period in previous years Measles Five or more cases of suspected measles in a sub-county or health facility in one month Three or more cases laboratory confirmed as Immunoglobulin M positive in a sub-county or health facility in a month Mumps Cases above 1 standard deviation from the 5-year mean data per geographical area Cases above 2 standard deviations from the 5-year mean data per geographical area Pertussis Cases above 1 standard deviation from the 5-year mean data per geographical area Cases above 2 standard deviations from the 5-year mean data per geographical area Q-fever Cases above 1 standard deviation from the 5-year mean data per geographical area Cases above 2 standard deviations from the 5-year mean data per geographical area Rabies 1 suspected case 1 confirmed case Rift Valley Fever (RVF) 1 suspected case 1 confirmed case SARI Cases above 1 standard deviation from the 5-year mean data per geographical area Cases above 2 standard deviations from the 5-year mean data per geographical area Schistosomiasis Cases above 1 standard deviation from the 5-year mean data per geographical area Cases above 2 standard deviations from the 5-year mean data per geographical area Salmonella 1 suspected case Double endemic threshold Viral Hepatitis Hepatitis A − 1 suspected case If there are more than 2 cases of jaundice in a village or an urban unit (of 1000 population) within a week. Yellow fever 1 suspected case 1 confirmed case Disease-specific caseload and mortality Overall, 464,008 cases and 6575 deaths were reported. The highest caseload and deaths were attributed to COVID-19, cholera, epidemic malaria, and leishmaniasis. COVID-19 overwhelmingly contributed to morbidity and mortality, representing 76% of the caseload and 86% of the deaths (Table 3 ). Table 3 Ranking of reported priority diseases by number of cases and deaths in Kenya, 2007–2022 Disease Number of cases Proportion of cases (%) Ranking Disease Number of deaths Proportion of deaths (%) Ranking COVID-19 353878 76.27 1 COVID-19 5687 86.49 1 Cholera 43205 9.31 2 Cholera 571 8.68 2 Epidemic Malaria 40116 8.65 3 SARI 94 1.43 3 Leishmaniasis 5878 1.27 4 Leishmaniasis 68 1.03 4 Hepatitis B 4986 1.07 5 Epidemic Malaria 36 0.55 5 Dengue Fever 4468 0.96 6 RVF 27 0.41 6 Measles 4061 0.88 7 Measles 21 0.32 7 Chikungunya 3504 0.76 8 Mumps 13 0.20 8 Influenza A 1374 0.30 9 Yellow Fever 12 0.18 9 SARI 823 0.18 10 Anthrax 11 0.17 10 Hepatitis E 491 0.11 11 Aflatoxicosis 10 0.15 11 Hepatitis A 334 0.07 12 Q Fever 6 0.09 12 RVF 232 0.05 13 Typhoid Fever 5 0.08 13 Anthrax 207 0.04 14 Hepatitis E 4 0.06 14 Yellow Fever 143 0.03 15 Dengue Fever 3 0.05 15 Salmonella 123 0.03 16 Influenza A 2 0.03 16 Pertussis 43 0.01 17 Hepatitis B 2 0.03 16 Q Fever 37 0.01 18 Rabies 1 0.02 17 AFP 34 0.01 19 Pertussis 1 0.02 17 Aflatoxicosis 31 0.01 20 Chikungunya 1 0.02 17 Mumps 21 0.00 21 Hepatitis A 0 0.00 18 Rabies 17 0.00 22 cVDPV2 0 0.00 18 cVDPV2 2 0.00 23 AFP 0 0.00 18 Total 464,008 Total 6,575 Outbreaks with the highest caseloads were COVID-19, cholera, malaria, kalaazar, hepatitis B, dengue, measles, and chikungunya (Fig. 1 ). COVID-19 was first detected in 2020 with most cases reported in 2021. All counties reported COVID-19 cases with Nairobi reporting the majority of cases (145,766 [41%]) overall. Cholera was reported annually except in 2011. Garissa had the most frequent annual reports of cholera, while Nairobi had the highest cumulative number of reported cholera cases 6,623(15%). Of the 40,116 epidemic malaria cases, 36,123 (82.0%) were reported from Elgeyo Marakwet in 2020. Leishmaniasis was reported in 2014, 2016, and yearly since 2019. The highest number of leishmaniasis cases, 2,858 (49%), were detected in Marsabit. Most cases of hepatitis B were reported in 2019, with West Pokot having the highest caseload 2,433 (47%) during the surveillance period. Dengue cases were reported in alternate years beginning in 2011 and were largely limited to Mombasa, Mandera, and Wajir counties. Chikungunya was first reported in 2016, then every 1–3 years, and was limited to Mandera, Mombasa, Wajir, Lamu, and Kilifi counties. Measles was reported yearly since 2012 with peaks in reporting in 2014, 2018, and 2020. Categorization of disease burden We obtained a median caseload of 334 cases and six deaths. When classified into 3 categories we observed that COVID-19, cholera, epidemic malaria, leishmaniasis, measles, and SARI were associated with high disease burden while salmonella, pertussis, rabies, and poliomyelitis were associated with the lowest burden of disease (Fig. 2 ). Annual trends in outbreak reporting The country recorded at least one outbreak each year between 2007 and 2022. In the first two years, only cholera was reported. Over time, the number of reported outbreaks gradually increased, except for sporadic declines in reporting in 6 of the 16 years of surveillance (Fig. 3 ). Overall we observed an increasing trend in the number of outbreaks reported over time (Mann Kendall test statistic 0.533, p-value = 0.006116). At the start of the surveillance period, there were reports received from four counties which were followed by a decline in the number of counties reporting outbreaks. In 2011, only one county reported an outbreak. Declines in county reporting were also observed in 2016 and 2019. The sharp increase in the number of counties reporting in 2020 was a result of COVID-19 reports from across all counties (Fig. 4 ). Nonetheless, we observed an overall increase in the number of counties reporting outbreaks over time (Mann Kendall test statistic 0.735, p value = 0.000115) Distribution of outbreaks by county Since 2007 all 47 counties have reported the occurrence of at least one outbreak. Of the 457 entries of outbreaks per county per year, Garissa, Nairobi, Nakuru, Wajir, Mandera, and Mombasa, accounted for a quarter of all entries. Garissa had the highest cumulative number of outbreak entries (32), while Kisii and Nyamira Counties had the least number (3 each) (Fig. 5 ). The average number of outbreaks reported per county was four. Nairobi reported the highest number of reported cases and deaths of all the outbreaks with approximately a third of the cases and a quarter of the deaths. Samburu reported the lowest number of cases, 331(0.07%) and 1 death, for the surveillance period. Discussion Over the last 16 years, we reported at least 23 outbreaks with increased frequency of reporting among the 47 counties. The number of outbreaks reported highlights the complex public health landscape in Kenya, while the frequent occurrence of epidemic-prone diseases illustrates the vulnerability of the country to large disease outbreaks. We also detected diseases earmarked for elimination and eradication though these diseases were not associated with high caseloads. Our classification of diseases determined that VPDs such as COVID-19, malaria, cholera, and measles were the most frequent causes of morbidity and mortality. Over time, there was an increase in the magnitude of outbreaks. The increase may be due to; (i) revision of the IDSR technical guidelines in 2012 and 2022 increased the number of priority diseases, conditions, and events for surveillance from 18 to 55, therefore increasing the number of outbreaks reported, (ii) improvements in surveillance activities across the country may have occurred after the transition to a devolved system of governance in 2013, and (iii) the government enforced regular reporting of COVID-19 and as a result counties, that had never previously reported any disease, reported cases of COVID-19. This suggests that improved surveillance rather than an increase in the occurrence of diseases led to the rise in reports over time. We observed the highest burden in urban counties; Nairobi, Nakuru, and Mombasa, and the North-Eastern counties; Garissa, Wajir, and Mandera. Nairobi, Mombasa, and Nakuru host 3 of the 4 cities in Kenya. These cities are overcrowded areas which are characterized by inadequate water and sanitation infrastructure ( 6 ). Nairobi and Mombasa serve as border transit hubs, attracting a constant influx of people from countries experiencing active disease outbreaks. Garissa, Wajir, and Mandera are classified as arid lands, with inadequate water and sanitation infrastructure. Although Garissa is sparsely populated, it has overcrowded refugee camps ( 7 ), ( 8 ). The country’s susceptibility to frequent outbreaks is worsened by inadequate access to safe water and sanitation, internal conflicts, food insecurity, limited access to health services, poor socio-economic status, and environmental conditions ( 9 ). To strategically address the needs of high-burden counties, a regional disease response hub should be established for the high-risk counties to preposition commodities and enhance timely and effective response to epidemics. Globally, the infectious diseases that have caused the highest number of outbreaks between 1996 to 2022, in order of magnitude, are COVID-19, pandemic influenza virus, cholera, acute poliomyelitis, and yellow fever (Torres Munguía et al., 2022). Nationally, we found that COVID-19, cholera, malaria, leishmaniasis, measles, and SARI were the most frequent causes of illness and death due to infectious diseases. Although in 2015, anthrax, trypanosomiasis, rabies, brucellosis, and RVF were identified as the top priority zoonotic diseases in Kenya ( 10 ), we found that influenza-A, dengue, and chikungunya were the more commonly occurring zoonotic diseases in the recent past. Limitations The use of routine surveillance data is affected by reporting rates, surveillance performance across counties, and the effect of long-term data archiving. Furthermore, the detection of diseases is limited by the availability of diagnostic tests which may affect reporting of some diseases. Conclusion The burden of disease outbreaks is increasing in magnitude and this underscores the need to bolster key aspects of Public Health preparedness and response. Strengthening the prevention of disease outbreaks by scaling up vaccination programs for VPDs and use of Early Warning and Reporting Systems. Enhancing preparedness by refining the IDSR strategy, capacity building frontline workers by use of diverse training approaches including simulation exercises. Augmenting response by recruiting and capacity-building Rapid Response Teams. Fortifying resilience by equipping and repositioning commodities in healthcare facilities and positioning regional disease response hubs to enhance timely and effective response to outbreaks. Declarations Ethics approval and consent to participate – Not Applicable Consent for publication – Not Applicable This study used aggregate data that did not contain any personal information about the participants, therefore, obtaining participants' consent was not applicable. This was not an experimental study and direct human data was not used, therefore, obtaining ethical approval is not applicable. The Disease Surveillance and Response Unit head approved the use of the data for the study and publication of the manuscript. Availability of data and materials The data that support the findings of this study are not publicly available due to the Ministry of Health Kenya's legal standards for data protection. Data are however available from the authors upon reasonable request and with permission of the Ministry of Health Kenya. Competing interests - The authors declare that they have no financial or non-financial competing interests related to the study or its publication. Funding – No funding Authors' contributions Farida Geteri* (corresponding author, [email protected] ) Affiliation: Disease Surveillance and Response Unit, Ministry of Health, Nairobi City, Kenya Role: Data analysis Interpretation of data Data visualization Contribution to the first draft Review of the final draft Jea nette Dawa Affiliation: Washington State University, Global Health Kenya, Nairobi City, Kenya Role: Data analysis Interpretation of data Data visualization Contribution to the first draft Review of the final draft John Gachohi Affiliation: Washington State University, Global Health Kenya, Nairobi City, Kenya Role: Data visualization Samuel Kadivane Affiliation: Disease Surveillance and Response Unit, Ministry of Health, Nairobi City, Kenya Role: Interpretation of data Data visualization Review of the draft Emmanuel Okunga Affiliation: Disease Surveillance and Response Unit, Ministry of Health, Nairobi City, Kenya Role: Conceptualization Data visualization Contribution to the first draft Review of the final draft References Armando, Juan A, et al. A global dataset of pandemic- and epidemic-prone disease outbreaks. Scientific Data. [Online] 2022. www.nature.com/scientificdata/. Hmwe, Kyu, et al. Global, regional, and national disability-adjusted life-years (DALYs) for 359 diseases and injuries and healthy life expectancy (HALE) for 195 countries and territories, 1990-2017: A systematic analysis for the Global Burden of Disease Study 2017. s.l. : Lancet, 2018. IHR. [Online] 2005. MOH Kenya. 3rd Edition IDSR Technical Guidelines. 2022. R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. [Online] 2022. URL https://www.R-project.org/). Reliefweb. Kenya: Cholera Outbreak - Operational update, Appeal No. MDRKE054 (22 July 2023). [Online] 2023. https://reliefweb.int/report/kenya/kenya-cholera-outbreak-operational-update-appeal-no-mdrke054-22-july-2023. Infotrack. Infotrack research and consulting. [Online] 2020. http://countytrak.infotrakresearch.com/north-eastern-region-2/. UNICEF Kenya. Humanitarian Situation Report No. 7, January - December 2022. [Online] 2022. https://reliefweb.int/report/kenya/unicef-kenya-humanitarian-situation-report-no-7-january-december-2022. Mboussou, F., et al. Infectious disease outbreaks in the African region: overview of events reported to the World Health Organization in 2018. s.l. : Cambridge, 2019. Munyua, Peninah, et al. Prioritization of Zoonotic Diseases in Kenya, 2015. 2015. Additional Declarations No competing interests reported. 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12:22:19","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":39676,"visible":true,"origin":"","legend":"\u003cp\u003eCategorization of disease outbreak burden in Kenya 2007-2022\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-3967744/v1/e8c4e0600835a7c18911c4c4.png"},{"id":53166070,"identity":"cb0972fb-6eb8-4896-a7a2-2f144f99b1e2","added_by":"auto","created_at":"2024-03-21 12:14:19","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":20507,"visible":true,"origin":"","legend":"\u003cp\u003eTrends of outbreaks 2007-2022\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-3967744/v1/c2a9577e8011aebc706a20eb.png"},{"id":53166072,"identity":"e5b053b2-4031-49fd-bc43-1eadff7929ee","added_by":"auto","created_at":"2024-03-21 12:14:19","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":23493,"visible":true,"origin":"","legend":"\u003cp\u003eCounties reporting outbreaks in Kenya, 2007-2022\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-3967744/v1/fe3c809da42bd35c2f2e1df6.png"},{"id":53166074,"identity":"4aef5c07-91e7-4404-88de-3a5756d12c26","added_by":"auto","created_at":"2024-03-21 12:14:19","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":71875,"visible":true,"origin":"","legend":"\u003cp\u003eFrequency of disease outbreaks reported by county in Kenya, 2007-2022\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-3967744/v1/0fc8c4d6e9171d00b06a0467.png"},{"id":67148673,"identity":"120ff58d-0ecb-4e7f-a01e-9f2acc31b7d1","added_by":"auto","created_at":"2024-10-21 16:05:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":885607,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3967744/v1/4ea456be-e8ec-4c5b-bb8e-6d18115e26ca.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A recent history of disease outbreaks in Kenya, 2007-2022: Findings from routine surveillance data","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDisease outbreaks cause significant health concerns globally, however, the occurrence of these outbreaks disproportionately affects world regions. Africa reports the highest number of outbreaks in the world (Torres Mungu\u0026iacute;a et al., 2022), accounting for 39% of all outbreaks in 2022 (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). In Africa, the common causes of epidemics are Vaccine-Preventable Diseases (VPDs), vector-borne, water-borne, and zoonotic diseases (Mboussou et al., 2019).\u003c/p\u003e \u003cp\u003eThe incidence and impact of diseases vary over time (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Despite a rising trend in non-communicable conditions worldwide, the relative burden of infectious diseases is continually changing. Specific infectious diseases may be more significant at the regional, national, or sub-national level while decreasing in importance globally. These differences are best captured by surveillance. Better awareness of the diseases most commonly affecting an area can lead to more accurate disease occurrence maps informed by local data. Ultimately, this leads to better preparedness and more efficient allocation of resources for prevention and control strategies.\u003c/p\u003e \u003cp\u003eSurveillance in Kenya is conducted through the Integrated Disease Surveillance and Response (IDSR) strategy adopted from the World Health Organisation. The strategy is designed to collect health data for multiple diseases and public health events using standardized data collection tools. The IDSR strategy ensures the reporting of priority diseases, conditions, and events to the national level in line with the International Health Regulations (IHR) requirements (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe present a summary of the burden of disease outbreaks in Kenya between 2007 and 2022 reported as part of the IDSR strategy. Our findings will provide valuable information for the development of sub-national disease occurrence maps and contribute to more efficient use of resources at the county level to inform public health prevention, preparedness, and response measures.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eIn 2023, we reviewed historical surveillance data held by the Disease Surveillance and Response Unit (DSRU) of the Ministry of Health, Kenya, 2007\u0026ndash;2022 using the list of priority diseases (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). When a suspected case of an outbreak is detected at the community or health facility level, a Community Health Assistant reports to the surveillance officer, who immediately notifies the Sub-County Disease Surveillance Coordinator (SCDSC). The SCDSC then reports to the County Disease Surveillance Coordinator (CDSC), who informs the national-level DSRU within 24 hours of the initial report. Line listing is initiated as soon as an outbreak is confirmed. An outbreak is confirmed when the number of cases exceeds the predefined action threshold and it is considered over when no new cases are reported within two incubation periods. For epidemic-prone diseases, an outbreak is over when the number of cases declines below the action threshold (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eList of priority diseases adapted from Kenya's integrated disease surveillance and response framework, 2021\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEpidemic Prone Diseases\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDiseases targeted for eradication and elimination\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDiseases, conditions, and events of Public Health Importance\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcute flaccid paralysis (Poliomyelitis)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGuinea worm Disease (Dracunculiasis)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAcute Malnutrition\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnthrax\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHuman African Trypanosomiasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAdverse events following immunization (AEFI)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBrucellosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLeprosy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAflatoxicosis\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBacterial Meningitis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLymphatic filariasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAnimal Bites (including Dog bites, snake bites, wild animals)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChikungunya\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMalaria*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCancers\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCholera\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMeasles\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDiabetes Mellitus (New cases)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDengue Fever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNeonatal tetanus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDiarrhoea with dehydration in children\u0026thinsp;\u0026lt;\u0026thinsp;5 years of age\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiarrhoea with blood (Shigella)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOnchocerciasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEpilepsy\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEbola/Marburg Haemorrhagic Fever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRabies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHepatitis A/B/C/E\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeishmaniasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTrachoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHypertension (New cases)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlague\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"10\" rowspan=\"11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMaternal Death\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRift Valley Fever (RVF)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNeonatal death\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmallpox (Variola)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNewly diagnosed HIV infection\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyphoid Fever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSexually transmitted infections (Gonorrhoea, Syphilis, Chlamydia, Herpes genitalia)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYellow fever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSchistosomiasis\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInfluenza Like Illness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSevere Pneumonia in Children under five-years-old\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInfluenza due to a new subtype\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSoil-transmitted helminths\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSARS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSubstance Abuse including Alcohol and other Drugs\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSevere Acute Respiratory Infections\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSuicides/ attempted suicides\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBacterial Meningitis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTrauma (Road traffic injury/ Fatality)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTuberculosis (MDR /XDR)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eAny public health event of national/regional/international concern (infectious, zoonotic, foodborne, chemical, radio-nuclear, or due to an unknown condition)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003e*For this study, data used was epidemic malaria data collected from the malaria epidemic-prone counties. Seasonal malaria data from endemic counties is reported separately.\u003c/em\u003e \u003c/p\u003e \u003cp\u003eWe summarized the archived data from 2007 to 2022, on the annual number of outbreaks, caseload, and deaths of the outbreaks in each of the 47 counties. We abstracted the data into an electronic spreadsheet.\u003c/p\u003e \u003cp\u003eTotals were presented in tables and figures where outbreaks and counties were ranked according to the frequency of outbreak occurrence, cases, and deaths. To classify the diseases into 3 categories of burden i.e., high, moderate, and low burden we obtained the median caseload and deaths per outbreak and used these values to classify each outbreak into one of the 3 categories. High-burden outbreaks had both the caseload and deaths higher than the median. Moderate-burden outbreaks had either the caseload or deaths higher than the median. Low-burden outbreaks had both the caseload and deaths lower than the median. To establish whether there were trends in the number of outbreaks detected and counties reporting over time, we conducted the Mann-Kendall test for trends. Analysis was conducted using Microsoft Excel and R (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eWe obtained 457 outbreaks occurrence entries over the 16 years, with most outbreaks occurring severally across the years. Seven(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e) entries in 2012, 2013, and 2014 had missing information on the counties affected. Twenty-three outbreaks were reported between 2007 and 2022. More than half, 13(59%) of the diseases were epidemic-prone diseases: COVID-19, cholera, epidemic malaria, leishmaniasis, dengue fever, measles, chikungunya, influenza A, severe acute respiratory illness (SARI), Rift Valley Fever (RVF), anthrax, yellow fever, and salmonella. Seven(32%) of the diseases; hepatitis A, B, and E, pertussis, aflatoxicosis, mumps, and Q-fever were diseases of public health importance. Two(9%) of the diseases were targeted for eradication and elimination: rabies and poliomyelitis including (acute flaccid paralysis [AFP] and vaccine-derived poliovirus 2 [VDPV2]). The reported outbreak alert and action thresholds are listed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eList of reported disease outbreaks alert and action thresholds, adapted from Kenya's integrated disease surveillance and response framework, 2021\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDisease\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAlert Threshold\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAction Threshold\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcute flaccid paralysis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 suspected case\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 confirmed case\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAflatoxicosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 suspected case\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 confirmed case\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnthrax\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 suspected case\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 confirmed case\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChikungunya\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 suspected case\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 confirmed case\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCholera\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 suspected case\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 confirmed case\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOVID-19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 confirmed cases within 2 weeks\u0026nbsp;in a previously unaffected area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePositivity rate\u0026thinsp;\u0026gt;\u0026thinsp;5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDengue fever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCases above 1 standard deviation from the 5-year mean data per geographical area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCases above 2 standard deviations from the 5-year mean data per geographical area\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInfluenza A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCases above 1 standard deviation from the 5-year mean data per geographical area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCases above 2 standard deviations from the 5-year mean data per geographical area\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeishmaniasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 suspected case\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 confirmed case\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMalaria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnusual increase in the number of new malaria cases or deaths as compared to the same period in the previous years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe number of new cases exceeds the upper limit of cases seen in a previous non-epidemic period in previous years\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeasles\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFive or more cases of suspected measles in a sub-county or health facility in one month\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThree or more cases laboratory confirmed as Immunoglobulin M positive in a sub-county or health facility in a month\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMumps\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCases above 1 standard deviation from the 5-year mean data per geographical area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCases above 2 standard deviations from the 5-year mean data per geographical area\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePertussis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCases above 1 standard deviation from the 5-year mean data per geographical area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCases above 2 standard deviations from the 5-year mean data per geographical area\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ-fever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCases above 1 standard deviation from the 5-year mean data per geographical area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCases above 2 standard deviations from the 5-year mean data per geographical area\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRabies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 suspected case\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 confirmed case\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRift Valley Fever (RVF)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 suspected case\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 confirmed case\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSARI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCases above 1 standard deviation from the 5-year mean data per geographical area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCases above 2 standard deviations from the 5-year mean data per geographical area\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSchistosomiasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCases above 1 standard deviation from the 5-year mean data per geographical area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCases above 2 standard deviations from the 5-year mean data per geographical area\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSalmonella\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 suspected case\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDouble endemic threshold\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eViral Hepatitis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHepatitis A \u0026minus;\u0026thinsp;1 suspected case\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIf there are more than 2 cases of jaundice in a village or an urban unit (of 1000 population) within a week.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYellow fever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 suspected case\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 confirmed case\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eDisease-specific caseload and mortality\u003c/h2\u003e \u003cp\u003eOverall, 464,008 cases and 6575 deaths were reported. The highest caseload and deaths were attributed to COVID-19, cholera, epidemic malaria, and leishmaniasis. COVID-19 overwhelmingly contributed to morbidity and mortality, representing 76% of the caseload and 86% of the deaths (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRanking of reported priority diseases by number of cases and deaths in Kenya, 2007\u0026ndash;2022\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDisease\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of cases\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProportion of cases (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRanking\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDisease\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNumber of deaths\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eProportion of deaths (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eRanking\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOVID-19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e353878\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCOVID-19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5687\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e86.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCholera\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCholera\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e571\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEpidemic Malaria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSARI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeishmaniasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5878\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLeishmaniasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHepatitis B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4986\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEpidemic Malaria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDengue Fever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4468\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRVF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeasles\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMeasles\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChikungunya\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3504\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMumps\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInfluenza A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1374\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYellow Fever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSARI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e823\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAnthrax\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHepatitis E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e491\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAflatoxicosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHepatitis A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ Fever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRVF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e232\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTyphoid Fever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnthrax\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHepatitis E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYellow Fever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDengue Fever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSalmonella\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eInfluenza A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePertussis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHepatitis B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ Fever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRabies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAFP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePertussis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAflatoxicosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eChikungunya\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMumps\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHepatitis A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRabies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ecVDPV2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecVDPV2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAFP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e464,008\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e6,575\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eOutbreaks with the highest caseloads were COVID-19, cholera, malaria, kalaazar, hepatitis B, dengue, measles, and chikungunya (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). COVID-19 was first detected in 2020 with most cases reported in 2021. All counties reported COVID-19 cases with Nairobi reporting the majority of cases (145,766 [41%]) overall. Cholera was reported annually except in 2011. Garissa had the most frequent annual reports of cholera, while Nairobi had the highest cumulative number of reported cholera cases 6,623(15%). Of the 40,116 epidemic malaria cases, 36,123 (82.0%) were reported from Elgeyo Marakwet in 2020. Leishmaniasis was reported in 2014, 2016, and yearly since 2019. The highest number of leishmaniasis cases, 2,858 (49%), were detected in Marsabit. Most cases of hepatitis B were reported in 2019, with West Pokot having the highest caseload 2,433 (47%) during the surveillance period. Dengue cases were reported in alternate years beginning in 2011 and were largely limited to Mombasa, Mandera, and Wajir counties. Chikungunya was first reported in 2016, then every 1\u0026ndash;3 years, and was limited to Mandera, Mombasa, Wajir, Lamu, and Kilifi counties. Measles was reported yearly since 2012 with peaks in reporting in 2014, 2018, and 2020.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eCategorization of disease burden\u003c/h2\u003e \u003cp\u003eWe obtained a median caseload of 334 cases and six deaths. When classified into 3 categories we observed that COVID-19, cholera, epidemic malaria, leishmaniasis, measles, and SARI were associated with high disease burden while salmonella, pertussis, rabies, and poliomyelitis were associated with the lowest burden of disease (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eAnnual trends in outbreak reporting\u003c/h2\u003e \u003cp\u003eThe country recorded at least one outbreak each year between 2007 and 2022. In the first two years, only cholera was reported. Over time, the number of reported outbreaks gradually increased, except for sporadic declines in reporting in 6 of the 16 years of surveillance (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eOverall we observed an increasing trend in the number of outbreaks reported over time (Mann Kendall test statistic 0.533, p-value\u0026thinsp;=\u0026thinsp;0.006116). At the start of the surveillance period, there were reports received from four counties which were followed by a decline in the number of counties reporting outbreaks. In 2011, only one county reported an outbreak. Declines in county reporting were also observed in 2016 and 2019. The sharp increase in the number of counties reporting in 2020 was a result of COVID-19 reports from across all counties (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Nonetheless, we observed an overall increase in the number of counties reporting outbreaks over time (Mann Kendall test statistic 0.735, p value\u0026thinsp;=\u0026thinsp;0.000115)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eDistribution of outbreaks by county\u003c/h2\u003e \u003cp\u003eSince 2007 all 47 counties have reported the occurrence of at least one outbreak. Of the 457 entries of outbreaks per county per year, Garissa, Nairobi, Nakuru, Wajir, Mandera, and Mombasa, accounted for a quarter of all entries. Garissa had the highest cumulative number of outbreak entries (32), while Kisii and Nyamira Counties had the least number (3 each) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The average number of outbreaks reported per county was four. Nairobi reported the highest number of reported cases and deaths of all the outbreaks with approximately a third of the cases and a quarter of the deaths. Samburu reported the lowest number of cases, 331(0.07%) and 1 death, for the surveillance period.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eOver the last 16 years, we reported at least 23 outbreaks with increased frequency of reporting among the 47 counties. The number of outbreaks reported highlights the complex public health landscape in Kenya, while the frequent occurrence of epidemic-prone diseases illustrates the vulnerability of the country to large disease outbreaks. We also detected diseases earmarked for elimination and eradication though these diseases were not associated with high caseloads. Our classification of diseases determined that VPDs such as COVID-19, malaria, cholera, and measles were the most frequent causes of morbidity and mortality.\u003c/p\u003e \u003cp\u003eOver time, there was an increase in the magnitude of outbreaks. The increase may be due to; (i) revision of the IDSR technical guidelines in 2012 and 2022 increased the number of priority diseases, conditions, and events for surveillance from 18 to 55, therefore increasing the number of outbreaks reported, (ii) improvements in surveillance activities across the country may have occurred after the transition to a devolved system of governance in 2013, and (iii) the government enforced regular reporting of COVID-19 and as a result counties, that had never previously reported any disease, reported cases of COVID-19. This suggests that improved surveillance rather than an increase in the occurrence of diseases led to the rise in reports over time.\u003c/p\u003e \u003cp\u003eWe observed the highest burden in urban counties; Nairobi, Nakuru, and Mombasa, and the North-Eastern counties; Garissa, Wajir, and Mandera. Nairobi, Mombasa, and Nakuru host 3 of the 4 cities in Kenya. These cities are overcrowded areas which are characterized by inadequate water and sanitation infrastructure (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Nairobi and Mombasa serve as border transit hubs, attracting a constant influx of people from countries experiencing active disease outbreaks. Garissa, Wajir, and Mandera are classified as arid lands, with inadequate water and sanitation infrastructure. Although Garissa is sparsely populated, it has overcrowded refugee camps (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e), (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). The country\u0026rsquo;s susceptibility to frequent outbreaks is worsened by inadequate access to safe water and sanitation, internal conflicts, food insecurity, limited access to health services, poor socio-economic status, and environmental conditions (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). To strategically address the needs of high-burden counties, a regional disease response hub should be established for the high-risk counties to preposition commodities and enhance timely and effective response to epidemics.\u003c/p\u003e \u003cp\u003eGlobally, the infectious diseases that have caused the highest number of outbreaks between 1996 to 2022, in order of magnitude, are COVID-19, pandemic influenza virus, cholera, acute poliomyelitis, and yellow fever (Torres Mungu\u0026iacute;a et al., 2022). Nationally, we found that COVID-19, cholera, malaria, leishmaniasis, measles, and SARI were the most frequent causes of illness and death due to infectious diseases. Although in 2015, anthrax, trypanosomiasis, rabies, brucellosis, and RVF were identified as the top priority zoonotic diseases in Kenya (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e), we found that influenza-A, dengue, and chikungunya were the more commonly occurring zoonotic diseases in the recent past.\u003c/p\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eThe use of routine surveillance data is affected by reporting rates, surveillance performance across counties, and the effect of long-term data archiving. Furthermore, the detection of diseases is limited by the availability of diagnostic tests which may affect reporting of some diseases.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe burden of disease outbreaks is increasing in magnitude and this underscores the need to bolster key aspects of Public Health preparedness and response. Strengthening the prevention of disease outbreaks by scaling up vaccination programs for VPDs and use of Early Warning and Reporting Systems. Enhancing preparedness by refining the IDSR strategy, capacity building frontline workers by use of diverse training approaches including simulation exercises. Augmenting response by recruiting and capacity-building Rapid Response Teams. Fortifying resilience by equipping and repositioning commodities in healthcare facilities and positioning regional disease response hubs to enhance timely and effective response to outbreaks.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate \u0026ndash; Not Applicable\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConsent for publication \u0026ndash; Not Applicable\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study used aggregate data that did not contain any personal information about the participants, therefore, obtaining participants\u0026apos; consent was not applicable.\u003c/p\u003e\n\u003cp\u003eThis was not an experimental study and direct human data was not used, therefore, obtaining ethical approval is not applicable.\u003c/p\u003e\n\u003cp\u003eThe Disease Surveillance and Response Unit head approved the use of the data for the study and publication of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are not publicly available due to the Ministry of Health Kenya\u0026apos;s legal standards for data protection. Data are however available from the authors upon reasonable request and with permission of the Ministry of Health Kenya.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e - The authors declare that they have no financial or non-financial competing interests related to the study or its publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding \u0026ndash;\u003c/strong\u003e No funding\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFarida Geteri*\u003c/strong\u003e (corresponding author, [email protected])\u003c/p\u003e\n\u003cp\u003eAffiliation: Disease Surveillance and Response Unit, Ministry of Health, Nairobi City, Kenya\u003c/p\u003e\n\u003cp\u003eRole: \u0026nbsp; \u0026nbsp; \u0026nbsp;Data analysis\u003c/p\u003e\n\u003cp\u003eInterpretation of data\u003c/p\u003e\n\u003cp\u003eData visualization\u003c/p\u003e\n\u003cp\u003eContribution to the first draft\u003c/p\u003e\n\u003cp\u003eReview of the final draft\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJea\u003c/strong\u003e\u003cstrong\u003enette Dawa\u003csup\u003e\u0026nbsp;\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAffiliation:\u0026nbsp;Washington State University, Global Health Kenya, Nairobi City, Kenya\u003c/p\u003e\n\u003cp\u003eRole: \u0026nbsp; \u0026nbsp;Data analysis\u003c/p\u003e\n\u003cp\u003eInterpretation of data\u003c/p\u003e\n\u003cp\u003eData visualization\u003c/p\u003e\n\u003cp\u003eContribution to the first draft\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Review of the final draft\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJohn Gachohi\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAffiliation:\u0026nbsp;Washington State University, Global Health Kenya, Nairobi City, Kenya\u003c/p\u003e\n\u003cp\u003eRole: \u0026nbsp; \u0026nbsp; \u0026nbsp;Data visualization\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSamuel Kadivane\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAffiliation: Disease Surveillance and Response Unit, Ministry of Health,\u0026nbsp;Nairobi City, Kenya\u003c/p\u003e\n\u003cp\u003eRole: \u0026nbsp; \u0026nbsp; \u0026nbsp;Interpretation of data\u003c/p\u003e\n\u003cp\u003eData visualization\u003c/p\u003e\n\u003cp\u003eReview of the draft\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEmmanuel Okunga\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAffiliation: Disease Surveillance and Response Unit, Ministry of Health, Nairobi City, Kenya\u003c/p\u003e\n\u003cp\u003eRole: \u0026nbsp; \u0026nbsp; \u0026nbsp;Conceptualization\u003c/p\u003e\n\u003cp\u003eData visualization\u003c/p\u003e\n\u003cp\u003eContribution to the first draft\u003c/p\u003e\n\u003cp\u003eReview of the final draft\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003e\u003cstrong\u003eArmando, Juan A, et al.\u003c/strong\u003e A global dataset of pandemic- and epidemic-prone disease outbreaks. \u003cem\u003eScientific Data. \u003c/em\u003e[Online] 2022. www.nature.com/scientificdata/.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eHmwe, Kyu, et al.\u003c/strong\u003e \u003cem\u003eGlobal, regional, and national disability-adjusted life-years (DALYs) for 359 diseases and injuries and healthy life expectancy (HALE) for 195 countries and territories, 1990-2017: A systematic analysis for the Global Burden of Disease Study 2017. \u003c/em\u003es.l. : Lancet, 2018.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eIHR.\u003c/strong\u003e [Online] 2005. \u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eMOH Kenya.\u003c/strong\u003e \u003cem\u003e3rd Edition IDSR Technical Guidelines. \u003c/em\u003e2022.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eR Core Team.\u003c/strong\u003e \u003cem\u003eR: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. \u003c/em\u003e[Online] 2022. URL https://www.R-project.org/).\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eReliefweb.\u003c/strong\u003e Kenya: Cholera Outbreak - Operational update, Appeal No. MDRKE054 (22 July 2023). [Online] 2023. https://reliefweb.int/report/kenya/kenya-cholera-outbreak-operational-update-appeal-no-mdrke054-22-july-2023.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eInfotrack.\u003c/strong\u003e Infotrack research and consulting. [Online] 2020. http://countytrak.infotrakresearch.com/north-eastern-region-2/.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eUNICEF Kenya.\u003c/strong\u003e Humanitarian Situation Report No. 7, January - December 2022. [Online] 2022. https://reliefweb.int/report/kenya/unicef-kenya-humanitarian-situation-report-no-7-january-december-2022.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eMboussou, F., et al.\u003c/strong\u003e \u003cem\u003eInfectious disease outbreaks in the African region: overview of events reported to the World Health Organization in 2018. \u003c/em\u003es.l. : Cambridge, 2019.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eMunyua, Peninah, et al.\u003c/strong\u003e \u003cem\u003ePrioritization of Zoonotic Diseases in Kenya, 2015. \u003c/em\u003e2015.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-research-notes","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"resn","sideBox":"Learn more about [BMC Research Notes](http://bmcresnotes.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/resn/default.aspx","title":"BMC Research Notes","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Priority disease, outbreak, surveillance, burden, Kenya","lastPublishedDoi":"10.21203/rs.3.rs-3967744/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3967744/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAfrica reports the highest number of outbreaks in the world, accounting for 39% of all outbreaks in 2022. The Integrated Disease Surveillance and Response strategy in Kenya ensures the reporting of outbreaks at the national level. We present a summary of the burden of reported disease outbreaks in Kenya, 2007\u0026ndash;2022.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe reviewed historical surveillance data, 2007\u0026ndash;2022. We summarized the annual caseload and deaths of the reported outbreaks per county. We classified the outbreaks into 3 categories i.e., high, moderate, and low burden. We conducted the Mann-Kendall test to detect trends in the number of outbreaks and counties reporting over time.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eTwenty-three outbreaks were reported. COVID-19, cholera, epidemic malaria, leishmaniasis, and measles were associated with high disease burden. The highest number of outbreaks reported in a single year was 10. Garissa, Nairobi, Nakuru, Wajir, Mandera, and Mombasa, had the majority of the outbreaks and caseload.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThere was an increase in the frequency and magnitude of outbreaks. This highlights the complex public health landscape and the vulnerability of the country to epidemics. The differences in outbreak occurrence among counties necessitate targeted and enhanced preventive, preparedness, and response interventions at the sub-national level to reduce the burden of outbreaks.\u003c/p\u003e","manuscriptTitle":"A recent history of disease outbreaks in Kenya, 2007-2022: Findings from routine surveillance data","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-21 12:14:15","doi":"10.21203/rs.3.rs-3967744/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-06-07T10:11:53+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-05-23T15:34:47+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-05-04T15:43:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"97091749509916475924935306668446281454","date":"2024-04-26T21:27:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"678fe9b3-c18a-4aec-9099-36e8edd0fab2","date":"2024-04-11T11:26:18+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-04-11T10:25:53+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-03-19T10:57:15+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-03-19T10:45:07+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-03-19T07:34:44+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Research Notes","date":"2024-02-18T18:32:46+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-research-notes","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"resn","sideBox":"Learn more about [BMC Research Notes](http://bmcresnotes.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/resn/default.aspx","title":"BMC Research Notes","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5c3584d3-44b2-4554-8e66-b4cfb3f51a2c","owner":[],"postedDate":"March 21st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-10-21T15:58:32+00:00","versionOfRecord":{"articleIdentity":"rs-3967744","link":"https://doi.org/10.1186/s13104-024-06930-5","journal":{"identity":"bmc-research-notes","isVorOnly":false,"title":"BMC Research Notes"},"publishedOn":"2024-10-15 15:56:52","publishedOnDateReadable":"October 15th, 2024"},"versionCreatedAt":"2024-03-21 12:14:15","video":"","vorDoi":"10.1186/s13104-024-06930-5","vorDoiUrl":"https://doi.org/10.1186/s13104-024-06930-5","workflowStages":[]},"version":"v1","identity":"rs-3967744","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3967744","identity":"rs-3967744","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}