A nowcasting framework for correcting for reporting delays in malaria surveillance

Time lags in reporting to national surveillance systems represent a major barrier for the control of infectious diseases, preventing timely decision making and resource allocation. This issue is particularly acute for infectious diseases like malaria, which often impact rural and remote communities the hardest. In Guyana, a country located in South America, poor connectivity among remote malaria-endemic regions hampers surveillance efforts, making reporting delays a key challenge for elimination. Here, we analyze 13 years of malaria surveillance data, identifying key correlates of time lags between clinical cases occurring and being added to the central data system. We develop nowcasting methods that use historical patterns of reporting delays to estimate occurred-but-not-reported monthly malaria cases. To assess their performance, we implemented them retrospectively, using only information that would have been available at the time of estimation, and found that they substantially enhanced the estimates of malaria cases. Specifically, we found that the best performing models achieved up to two-fold improvements in accuracy (or error reduction) over known cases in selected regions. Our approach provides a simple, generalizable tool to improve malaria surveillance in endemic countries and is currently being implemented to help guide existing resource allocation and elimination efforts. Author summaryInfectious disease monitoring systems worldwide are often threatened by severe reporting delays, hindering resource planning efforts, particularly for endemic diseases like malaria and in remote localities. In Guyana, a country located in South America, regions facing infrastructural barriers and consisting of highly mobile or socially disadvantaged populations oftentimes face the greatest delays in reporting local health facility data to the country's central database. To better characterize and address this issue, we analyzed time and spatial trends in malaria reporting delays across administrative regions and implement methods to transform incomplete case data to more accurate current case counts. We first identify factors which significantly spatially overlap with areas marked by increased reporting delays. We then introduce methods which build on prior case information only available at the time of the target month of interest to anticipate the total number of cases to occur that month, a fraction of which are not known until the subsequent month(s). We compared our regional monthly estimates with eventually reported cases and found that they were up to two times more accurate than case counts known at the month of reporting. Our models provide a widely accessible and adaptable tool for improving on malaria surveillance efforts.