Regional variation and epidemiological insights in malaria underestimation in Cameroon

Malaria, caused by Plasmodium parasites and transmitted by female Anopheles mosquitoes, is most common in tropical regions, especially in Sub-Saharan Africa. Despite significant global effort to control and eradicate the disease, many cases and deaths are still reported yearly. These efforts are hindered by several factors, including the severe underestimation of cases and deaths, especially in Africa, making it difficult to assess the disease burden accurately. We used a mathematical model of malaria, incorporating the underestimation of cases and seasonality in mosquito biting rate, to study the disease dynamics in Cameroon. Using a Bayesian inference framework, we calibrated our model to the monthly reported malaria cases in ten regions of Cameroon from January 2019 to December 2021 to quantify the underestimation of cases and estimate other important epidemiological parameters. We performed Hierarchical Clustering on Principal Components analysis to understand regional disparities, looking at underestimation rates, population sizes, healthcare personnel, and healthcare facilities per 1,000 people. We found varying levels of underestimation of cases across regions, with the East region having the lowest underestimation (14%) and the Northwest region with the highest (70%). The mosquito biting rate peaks once every year in most of the regions, except in the Northwest region where it peaks every 6.02 months and in Littoral every 15 months. We estimated a median mosquito biting rate of over five bites per day for most of the regions with Littoral having the highest (9.86 bites/day). Two regions have rates below five bites per day: Adamawa (4.78 bites/day) and East (4.64 bites/day). The notably low estimation of malaria cases in Cameroon underscore the pressing requirement to bolster reporting and surveillance systems. Regions in Cameroon display a range of unique features, which may contribute to the differing levels of malaria underestimation. These distinctions should be considered when evaluating the efficacy of community-based interventions. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This research is funded by Canada's International Development Research Centre (IDRC) (Grant No. 109981). JDK acknowledges support from New Frontier in Research Fund-Exploratory (Grant No. NFRFE-2021-00879) and NSERC Discovery Grant (Grant No. RGPIN-2022-04559). Portions of this work were performed at the Los Alamos National Laboratory under the auspices of the US Department of Energy contract 89233218CNA000001 and supported by NIH grant R01-OD011095. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes The monthly reported cases of malaria in Cameroon were obtained from DHIS2 ( https://dhis2.org), and are available within the article and its Supplementary material as well as on the link below: https://acadic.org/africa-in-data/. DHIS2 is an open-source, web-based software platform for data collection, management, and analysis in all government hospitals and public health centers in the countries where aggregation of health data from both government and non-government hospitals is collected. The Bayesian inference code used for our model calibration is available on https://github.com/iyaniwura/Malaria\_Model\_Fit_Code [https://github.com/iyaniwura/Malaria\_Model\_Fit_Code][1] [1]: https://github.com/iyaniwura/Malaria_Model_Fit_Code