Background Despite significant global effort to control and eradicate malaria,many cases and deaths are still reported yearly.These efforts are hindered by several factors,including the severe underestimation of cases...Background Despite significant global effort to control and eradicate malaria,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.Methods We used a mathematical model,incorporating the underestimation of cases and seasonality in mosquito biting rate,to study the malaria dynamics in Cameroon.Using a Bayesian inference framework,we calibrated our model to the monthly reported malaria cases in ten regions of Cameroon from 2019 to 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 1000 people.Results We found varying levels of case underestimation across regions,with the East region having the lowest(14%)and the Northwest having the highest(70%).The mosquito biting rate peaks once every year in most regions,except in the Northwest where it peaks every 6.02 months and in Littoral every 15 months.We estimated a median mosquito biting rate of over 5 bites/day for most regions with Littoral having the highest(9.86 bites/day).Two regions have rates below five:Adamawa(4.78 bites/day)and East(4.64 bites/day).Conclusions The low case estimation underscores the pressing requirement to bolster reporting and surveillance systems.Regions in Cameroon display a range of unique features contributing to the differing levels of underestimation.These distinctions should be considered when evaluating the efficacy of community-based interventions.展开更多
基金support from New Frontier in Research Fund-Exploratory(Grant No.NFRFE-2021-00879)NSERC Discovery Grant(Grant No.RGPIN-2022-04559)+1 种基金Portions of this work were performed at the Los Alamos National Laboratory under the auspices of the US Department of Energy contract 89233218CNA000001supported by NIH grant R01-OD011095.
文摘Background Despite significant global effort to control and eradicate malaria,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.Methods We used a mathematical model,incorporating the underestimation of cases and seasonality in mosquito biting rate,to study the malaria dynamics in Cameroon.Using a Bayesian inference framework,we calibrated our model to the monthly reported malaria cases in ten regions of Cameroon from 2019 to 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 1000 people.Results We found varying levels of case underestimation across regions,with the East region having the lowest(14%)and the Northwest having the highest(70%).The mosquito biting rate peaks once every year in most regions,except in the Northwest where it peaks every 6.02 months and in Littoral every 15 months.We estimated a median mosquito biting rate of over 5 bites/day for most regions with Littoral having the highest(9.86 bites/day).Two regions have rates below five:Adamawa(4.78 bites/day)and East(4.64 bites/day).Conclusions The low case estimation underscores the pressing requirement to bolster reporting and surveillance systems.Regions in Cameroon display a range of unique features contributing to the differing levels of underestimation.These distinctions should be considered when evaluating the efficacy of community-based interventions.
