Malaria predictions based on seasonal climate forecasts in South Africa: A time series distributed lag nonlinear model

Abstract

Although there have been enormous demands and efforts to develop an early warning system for malaria, no sustainable system has remained. Well-organized malaria surveillance and high-quality climate forecasts are required to sustain a malaria early warning system in conjunction with an effective malaria prediction model. We aimed to develop a weather-based malaria prediction model using a weekly time-series data including temperature, precipitation, and malaria cases from 1998 to 2015 in Vhembe, Limpopo, South Africa and apply it to seasonal climate forecasts. The malaria prediction model performed well for short-term predictions (correlation coefficient, r > 0.8 for 1- and 2-week ahead forecasts). The prediction accuracy decreased as the lead time increased but retained fairly good performance (r > 0.7) up to the 16-week ahead prediction. The demonstration of the malaria prediction process based on the seasonal climate forecasts showed the short-term predictions coincided closely with the observed malaria cases. The weather-based malaria prediction model we developed could be applicable in practice together with skillful seasonal climate forecasts and existing malaria surveillance data. Establishing an automated operating system based on real-time data inputs will be beneficial for the malaria early warning system, and can be an instructive example for other malaria-endemic areas.

Data-driven malaria prevalence prediction in large densely populated urban holoendemic sub-Saharan West Africa

“…We assembled our full Ibadan dataset, denoted by D, by aggregating data for each month from January 1996 to December 2017 (22 years), creating thus a total of 264 (22 × 12) entries (Table 1), each containing the following 15 variables (Table 2) Malaria screening. Malaria parasites (MPs) were detected and counted by microscopy following Giemsa staining of thick and thin blood films [8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24]27 . The criterion for declaring a participant to be malaria parasitefree was no detectable parasites in 100 high-power (100×) fields in both thick and thin films.…”

“…Strikingly, none of the systems above have been derived from a care pathway support perspective, nor they have been deployed or are in clinical use. One recent study 17 from a very-low seasonal non-holoendemic region reinforces the fact that although there have been enormous demands and efforts to develop predictive systems for malaria, no sustainable approach has been created. Altogether, this has translated to a lack of understanding concerning how regionally accurate short-lead predictions could influence the delivery of clinical malaria care pathways in resource constrained urban sub-Saharan healthcare systems.…”

Exploring public awareness of the current and future malaria risk zones in South Africa under climate change: a pilot study

“…While the regularly shifting extent of the malaria risk zone is posited as one of the reasons for a poor awareness among respondents, it could be argued that under climate change, an even more frequent updating and reporting of risk may be beneficial. Seasonal malaria forecasts are being produced for South Africa by local researchers (Kim et al 2019 ; Landman et al 2020 ), which could be presented to the public biannually to refine malaria prevention behaviour. Frequent and standardized government publication of season-specific information would potentially result in a greater reliance on these resources over word-of-mouth based on travel which occurred less recently.…”

“…Mathematical models have been developed to explore the role of climate in malaria incidence and to aid in projecting future changes in malaria risk distribution, and the risk of transmission (Cella et al 2019 ; Eikenberry and Gumel 2019 ). At a local scale, seasonal malaria forecast models are being developed for South Africa (Kim et al 2019 ; Landman et al 2020 ). The modelling of the climate impact on malaria distribution and incidence and projections for future incidence are valuable in developing public health policies, and in communication to the public.…”

Interannual Climate Variability and Malaria in Mozambique

“…While these attempts often utilize observed and forecasted weather and climate information, the usefulness of these inputs has yielded mixed results (Thomson et al, 2018). Conventional attempts to develop a malaria early warning system incorporating meteorological factors often aim to produce statistical models by focusing on short-term lead-times (i.e., weeks to 2 months of advance notice) between observed precipitation and temperature and their interactions with observed malaria incidence (e.g., Kim et al, 2019;Midekisa et al, 2012;Teklehaimanot et al, 2004;Xiang et al, 2018). Mabaso et al (2012) critically reviewed 35 publications and noted that nearly all examined studies agreed that meteorological conditions were a crucial factor in malaria epidemics, with an overarching focus on precipitation.…”