{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T10:29:25Z","timestamp":1785407365482,"version":"3.56.0"},"reference-count":41,"publisher":"AOSIS","issue":"1","license":[{"start":{"date-parts":[[2025,2,28]],"date-time":"2025-02-28T00:00:00Z","timestamp":1740700800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:p>Background:\u00a0Dengue fever (DF), transmitted by\u00a0Aedes\u00a0mosquitoes, remains a major public health concern in tropical and subtropical regions. Understanding the influence of climatic variables on DF incidence is essential for improving outbreak prediction and control measures.\n\nAim:\u00a0This study analysed the impact of climatic factors on DF incidence in Kenya using a Zero-Inflated Poisson (ZIP) model.\n\nSetting:\u00a0The study focused on DF cases in Kenya from 2019 to 2021.\n\nMethods:\u00a0A ZIP model was applied to monthly dengue case data and associated climatic variables, such as temperature, rainfall, and humidity. The model addresses over-dispersion and excess zeros in the data, providing a more accurate depiction of DF dynamics.\n\nResults:\u00a0The ZIP model revealed significant associations between climatic variables and DF incidence. Humidity (\u03b2 = 0.0578, standard error [s.e.] = 0.0024,\u00a0z\u00a0= 24.157,\u00a0p\u00a0&lt; 2e-16) and temperature (\u03b2 = 0.0558, s.e. = 0.0053,\u00a0z\u00a0= 10.497,\u00a0p\u00a0&lt; 0.01) showed a positive relationship with dengue cases, while rainfall (\u03b2 = \u20130.0045, s.e. = 0.0003,\u00a0z\u00a0= \u201316.523,\u00a0p\u00a0&lt; 0.01) had a significant negative effect. The over-dispersion test confirmed excess variability in the data (O statistic = 456.3,\u00a0p\u00a0= 0.004), and the Vuong test supported the use of the ZIP model over a standard Poisson model. Model comparison indicated superior fit for the ZIP model (akaike information criterion [AIC] = 5230.959 vs. 27061.367 for Poisson), effectively accounting for zero-inflation.\n\nConclusion:\u00a0The results suggest that higher humidity and temperature favor dengue transmission, while heavy rainfall may disrupt mosquito breeding, reducing cases. These findings provide a basis for targeted public health interventions.\n\nContribution:\u00a0This study enhances understanding of DF-climate interactions in Kenya, supporting the application of ZIP modelling for improved disease surveillance and control strategies.<\/jats:p>","DOI":"10.4102\/jphia.v16i1.781","type":"journal-article","created":{"date-parts":[[2025,3,13]],"date-time":"2025-03-13T07:37:38Z","timestamp":1741851458000},"update-policy":"https:\/\/doi.org\/10.4102\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Analysing dengue fever spread in Kenya using the Zero-Inflated Poisson model"],"prefix":"10.4102","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3740-8865","authenticated-orcid":false,"given":"Lameck Ondieki","family":"Agasa","sequence":"first","affiliation":[{"id":[{"id":"https:\/\/ror.org\/053stv828","id-type":"ROR","asserted-by":"publisher"}],"name":"Kisii University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7019-6542","authenticated-orcid":false,"given":"Faith","family":"Thuita","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/02y9nww90","id-type":"ROR","asserted-by":"publisher"}],"name":"University of Nairobi"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0700-9468","authenticated-orcid":false,"given":"Thomas","family":"Achia","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/047dnqw48","id-type":"ROR","asserted-by":"publisher"}],"name":"Strathmore University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-7500-5777","authenticated-orcid":false,"given":"Antony","family":"Karanja","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/028r4zr88","id-type":"ROR","asserted-by":"publisher"}],"name":"Multimedia University of Kenya"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2580","published-online":{"date-parts":[[2025,2,28]]},"reference":[{"key":"ref0","doi-asserted-by":"crossref","unstructured":"Bhatt S, Gething PW, Brady OJ, et al. The global distribution and burden of dengue. Nature. 2013;496(7446):504\u2013507. https:\/\/doi.org\/10.1038\/nature12060","DOI":"10.1038\/nature12060"},{"key":"ref1","doi-asserted-by":"crossref","unstructured":"Brady OJ, Gething PW, Bhatt S, et al. Refining the global spatial limits of DENV transmission by evidence-based consensus. PLOS Negl Trop Dis. 2012;6(8):e1760. https:\/\/doi.org\/10.1371\/journal.pntd.0001760","DOI":"10.1371\/journal.pntd.0001760"},{"key":"ref2","doi-asserted-by":"crossref","unstructured":"Khan MB, Yang Z-Y, Lin C-Y, et al. Dengue overview: An updated systemic review. J Infect Public Health. 2023;16(10):1625\u20131642. https:\/\/doi.org\/10.1016\/j.jiph.2023.08.001","DOI":"10.1016\/j.jiph.2023.08.001"},{"key":"ref3","doi-asserted-by":"crossref","unstructured":"Sirisena PDNN, Mahilkar S, Sharma C, Jain J, Sunil S. Concurrent dengue infections: Epidemiology & clinical implications. Indian J Med Res. 2021;154(5):669\u2013679. https:\/\/doi.org\/10.4103\/ijmr.IJMR_1219_18","DOI":"10.4103\/ijmr.IJMR_1219_18"},{"key":"ref4","doi-asserted-by":"crossref","unstructured":"Zerfu B, Kassa T, Legesse M. Epidemiology, biology, pathogenesis, clinical manifestations, and diagnosis of dengue virus infection, and its trend in Ethiopia: A comprehensive literature review. Trop Med Health. 2023;51:11. https:\/\/doi.org\/10.1186\/s41182-023-00504-0","DOI":"10.1186\/s41182-023-00504-0"},{"key":"ref5","unstructured":"World Health Organization. Dengue and severe dengue [homepage on the Internet]. Geneva: World Health Organization; 2022 [cited 2024 Sep 01]. Available from: https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/dengue-and-severe-dengue"},{"key":"ref6","doi-asserted-by":"crossref","unstructured":"Jing Q, Wang M. Dengue epidemiology. Glob Health J. 2019;3(2):37\u201345. https:\/\/doi.org\/10.1016\/j.glohj.2019.06.002","DOI":"10.1016\/j.glohj.2019.06.002"},{"key":"ref7","doi-asserted-by":"crossref","unstructured":"Hasan S, Jamdar SF, Alalowi M, Al Ageel Al Beaiji SM. Dengue virus: A global human threat: Review of literature. J Int Soc Prev Community Dent. 2016;6(1):1\u20136. https:\/\/doi.org\/10.4103\/2231-0762.175416","DOI":"10.4103\/2231-0762.175416"},{"key":"ref8","doi-asserted-by":"crossref","unstructured":"Motsay M, Saputo S. Acid adaptation alters Streptococcus mutans drug susceptibility profile. Microbe. 2024;2:100028. https:\/\/doi.org\/10.1016\/j.microb.2023.100028","DOI":"10.1016\/j.microb.2023.100028"},{"key":"ref9","doi-asserted-by":"crossref","unstructured":"Fernandez Cerna EA, Sherman C, Martinez MM. Dengue fever in a one health perspective. In: Speran\u00e7a MA, editor, Dengue fever in a one health perspective - latest research and recent advances. IntechOpen; 2023. https:\/\/doi.org\/10.5772\/intechopen.109603","DOI":"10.5772\/intechopen.109603"},{"key":"ref10","doi-asserted-by":"crossref","unstructured":"Tyhotyholo T, Ncube B. The rhetoric of community participation in urban South African water governance. Util Policy. 2023;82:101573. https:\/\/doi.org\/10.1016\/j.jup.2023.101573","DOI":"10.1016\/j.jup.2023.101573"},{"key":"ref11","doi-asserted-by":"crossref","unstructured":"Wang Y, Zhao S, Wei Y, et al. Impact of climate change on dengue fever epidemics in South and Southeast Asian settings: A modelling study. Infect Dis Model. 2023;8(3):645\u2013655. https:\/\/doi.org\/10.1016\/j.idm.2023.05.008","DOI":"10.1016\/j.idm.2023.05.008"},{"key":"ref12","doi-asserted-by":"crossref","unstructured":"Kulkarni MA, Duguay C, Ost K. Charting the evidence for climate change impacts on the global spread of malaria and dengue and adaptive responses: A scoping review of reviews. Global Health. 2022;18:1. https:\/\/doi.org\/10.1186\/s12992-021-00793-2","DOI":"10.1186\/s12992-021-00793-2"},{"key":"ref13","doi-asserted-by":"crossref","unstructured":"Kraemer MU, Sinka ME, Duda KA, et al. The global distribution of the arbovirus vectors Aedes aegypti and Ae. albopictus. elife. 2015;4:e08347.","DOI":"10.7554\/eLife.08347"},{"key":"ref14","doi-asserted-by":"crossref","unstructured":"Xu C, Xu J, Wang L. Long-term effects of climate factors on dengue fever over a 40-year period. BMC Public Health. 2024;24:1451. https:\/\/doi.org\/10.1186\/s12889-024-18869-0","DOI":"10.1186\/s12889-024-18869-0"},{"key":"ref15","doi-asserted-by":"crossref","unstructured":"Martens WJ, Niessen LW, Rotmans J, Jetten TH, McMichael AJ. Potential impact of global climate change on malaria risk. Environ Health Perspect. 1995;103(5):458\u2013464. https:\/\/doi.org\/10.1289\/ehp.95103458","DOI":"10.1289\/ehp.95103458"},{"key":"ref16","doi-asserted-by":"crossref","unstructured":"Monintja TCN, Arsin AA, Amiruddin R, Syafar M. Analysis of temperature and humidity on dengue hemorrhagic fever in Manado Municipality. Gac Sanit. 2021;35(suppl. 2):S330\u2013S333. https:\/\/doi.org\/10.1016\/j.gaceta.2021.07.020","DOI":"10.1016\/j.gaceta.2021.07.020"},{"key":"ref17","doi-asserted-by":"crossref","unstructured":"Baril C, Pilling BG, Mikkelsen MJ, et al. The influence of weather on the population dynamics of common mosquito vector species in the Canadian Prairies. Parasit Vectors. 2023;16:153. https:\/\/doi.org\/10.1186\/s13071-023-05760-x","DOI":"10.1186\/s13071-023-05760-x"},{"key":"ref18","doi-asserted-by":"crossref","unstructured":"Dommar CJ, Lowe R, Robinson M, Rod\u00f3 X. An agent-based model driven by tropical rainfall to understand the spatio-temporal heterogeneity of a chikungunya outbreak. Acta Trop. 2014;129:61\u201373. https:\/\/doi.org\/10.1016\/j.actatropica.2013.08.004","DOI":"10.1016\/j.actatropica.2013.08.004"},{"key":"ref19","unstructured":"Chandrakantha M. Meteorological parameters influencing dengue fever in Colombo, Sri Lanka. J Trop Med Hyg. 2019;92(5):735\u2013742."},{"key":"ref20","doi-asserted-by":"crossref","unstructured":"Mukhaiyar M, Ali A, Ningsih S. Forecasting dengue fever cases based on temperature and cumulative rainfall data in Bandung, West Java, Indonesia. Int J Environ Res Public Health. 2022;19(1):102\u2013115. https:\/\/doi.org\/10.3390\/ijerph19010002","DOI":"10.3390\/ijerph19010002"},{"key":"ref21","unstructured":"Sinaga T, Lestari DC, Utami A. The influence of climatic factors on dengue fever incidence in endemic regions: A case study from Indonesia. Glob Health Action. 2021;14(1):184\u2013192."},{"key":"ref22","doi-asserted-by":"crossref","unstructured":"Lee JH, Han G, Fulp WJ, Giuliano AR. Analysis of overdispersed count data: Application to the human papillomavirus infection in Men (HIM) study. Epidemiol Infect. 2012;140(6):1087\u20131094. https:\/\/doi.org\/10.1017\/S095026881100166X","DOI":"10.1017\/S095026881100166X"},{"key":"ref23","doi-asserted-by":"crossref","unstructured":"Broek JVD. A score test for zero inflation in a Poisson distribution. Biometrics. 1995;51(2):738\u2013743. https:\/\/doi.org\/10.2307\/2532959","DOI":"10.2307\/2532959"},{"key":"ref24","doi-asserted-by":"crossref","unstructured":"He H, Zhang H, Ye P, Tang W. A test of inflated zeros for Poisson regression models. Stat Methods Med Res. 2019;28(4):1157\u20131169. https:\/\/doi.org\/10.1177\/0962280217749991","DOI":"10.1177\/0962280217749991"},{"key":"ref25","doi-asserted-by":"crossref","unstructured":"Yau KK, Lee AH. Zero-Inflated Poisson regression with random effects to evaluate an occupational injury prevention programme. Stat Med. 2001;20(19):2907\u20132920. https:\/\/doi.org\/10.1002\/sim.860","DOI":"10.1002\/sim.860"},{"key":"ref26","doi-asserted-by":"crossref","unstructured":"Cheung YB. Zero-inflated models for regression analysis of count data: A study of growth and development. Stat Med. 2002;21(10):1461\u20131469. https:\/\/doi.org\/10.1002\/sim.1088","DOI":"10.1002\/sim.1088"},{"key":"ref27","doi-asserted-by":"crossref","unstructured":"Lim JK, Matendechero SH, Alexander N, et al. Clinical and epidemiologic characteristics associated with dengue fever in Mombasa, Kenya. Int J Infect Dis. 2020; 100:207\u2013215. https:\/\/doi.org\/10.1016\/j.ijid.2020.08.074","DOI":"10.1016\/j.ijid.2020.08.074"},{"key":"ref28","unstructured":"Johnson DT, Muriithi G, Nyambura R. Dengue fever outbreak in Kenya: Clinical and epidemiological findings. East Afr Med J. 1982;59(4):289\u2013295."},{"key":"ref29","doi-asserted-by":"crossref","unstructured":"Obonyo GS, Ochieng C, Nyamweya M. Dengue fever in Kenya: Trends and patterns of the 2017 outbreak. Afr Health Sci. 2018;18(1):65\u201372. https:\/\/doi.org\/10.4314\/ahs.v18i1.9","DOI":"10.4314\/ahs.v18i1.9"},{"key":"ref30","unstructured":"Ellis R, Mwaura J, Barasa J. Recurrent dengue fever outbreaks in Kenya: A historical perspective. J Trop Med Parasitol. 2015;33(2):102\u2013111."},{"key":"ref31","unstructured":"Ministry of Health, Kenya. Standard case definitions for priority diseases in Kenya: Integrated disease surveillance and response (IDSR), 2018 [homepage on the Internet]. [cited 2024 Sep 01]. Available from: http:\/\/guidelines.health.go.ke:8000\/media\/Standard_Case_Definitions_for_Priority_Diseases_in_Kenya-_Integ.pdf"},{"key":"ref32","doi-asserted-by":"crossref","unstructured":"Lim JK, Matendechero SH, Alexander N, et al. Clinical and epidemiologic characteristics associated with dengue fever in Mombasa, Kenya. Int J Infect Dis. 2020;100:207\u2013215. https:\/\/doi.org\/10.1016\/j.ijid.2020.08.074","DOI":"10.1016\/j.ijid.2020.08.074"},{"key":"ref33","doi-asserted-by":"crossref","unstructured":"Ng\u2019etich AKS, Voyi K, Mutero CM. Assessment of surveillance core and support functions regarding neglected tropical diseases in Kenya. BMC Public Health. 2021;21(1):142. https:\/\/doi.org\/10.1186\/s12889-021-10185-1","DOI":"10.1186\/s12889-021-10185-1"},{"key":"ref34","doi-asserted-by":"crossref","unstructured":"Muinga N, Magare S, Monda J, et al. Digital health systems in Kenyan public hospitals: A mixed-methods survey. BMC Med Inform Decis Mak. 2020;20(1):2. https:\/\/doi.org\/10.1186\/s12911-019-1005-7","DOI":"10.1186\/s12911-019-1005-7"},{"key":"ref35","unstructured":"DHIS2. DHIS2 implementation guide [homepage on the Internet]. 2018 [cited 2024 Sep 01]. Available from: https:\/\/docs.dhis2.org\/en\/full\/implement\/dhis2-implementation-guide.html"},{"key":"ref36","doi-asserted-by":"crossref","unstructured":"Yang Z, Hardin JW, Addy CL. Testing overdispersion in the zero-inflated Poisson model. J Stat Plan Inference. 2009;139(9):3340\u20133353. https:\/\/doi.org\/10.1016\/j.jspi.2009.03.016","DOI":"10.1016\/j.jspi.2009.03.016"},{"key":"ref37","doi-asserted-by":"crossref","unstructured":"Choi Y, Tang CS, McIver L, et al. Effects of weather factors on dengue fever incidence and implications for interventions in Cambodia. BMC Public Health. 2016;16:241. https:\/\/doi.org\/10.1186\/s12889-016-2923-2","DOI":"10.1186\/s12889-016-2923-2"},{"key":"ref38","doi-asserted-by":"crossref","unstructured":"Andhikaputra G, Lin YH, Wang YC. Effects of temperature, rainfall, and El Ni\u00f1o southern oscillations on dengue-like-illness incidence in Solomon Islands. BMC Infect Dis. 2023;23:206. https:\/\/doi.org\/10.1186\/s12879-023-08188-x","DOI":"10.1186\/s12879-023-08188-x"},{"key":"ref39","doi-asserted-by":"crossref","unstructured":"Cheng Q, Jing Q, Collender PA, et al. Prior water availability modifies the effect of heavy rainfall on dengue transmission: A time series analysis of passive surveillance data from southern China. Front Public Health. 2023;11:1287678. https:\/\/doi.org\/10.3389\/fpubh.2023.1287678","DOI":"10.3389\/fpubh.2023.1287678"},{"key":"ref40","doi-asserted-by":"crossref","unstructured":"Nosrat C, Altamirano J, Anyamba A, et al. Impact of recent climate extremes on mosquito-borne disease transmission in Kenya. PLoS Negl Trop Dis. 2021;15(3):e0009182. https:\/\/doi.org\/10.1371\/journal.pntd.0009182","DOI":"10.1371\/journal.pntd.0009182"}],"container-title":["Journal of Public Health in Africa"],"original-title":[],"link":[{"URL":"https:\/\/publichealthinafrica.org\/index.php\/jphia\/article\/viewFile\/781\/html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/publichealthinafrica.org\/index.php\/jphia\/article\/viewFile\/781\/epub","content-type":"application\/epub+zip","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/publichealthinafrica.org\/index.php\/jphia\/article\/viewFile\/781\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/publichealthinafrica.org\/index.php\/jphia\/article\/viewFile\/781\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/publichealthinafrica.org\/index.php\/jphia\/article\/view\/781\/1536","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T14:40:34Z","timestamp":1781275234000},"score":1,"resource":{"primary":{"URL":"https:\/\/publichealthinafrica.org\/index.php\/jphia\/article\/view\/781"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,28]]},"references-count":41,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,1,1]]}},"URL":"https:\/\/doi.org\/10.4102\/jphia.v16i1.781","relation":{},"ISSN":["2038-9922","2038-9930"],"issn-type":[{"value":"2038-9922","type":"print"},{"value":"2038-9930","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2,28]]},"article-number":"a781"}}