Malaria Temporal Variation and Modelling Using Time-Series in Sussundenga District, Mozambique

Abstract

Malaria is one of the leading causes of morbidity and mortality in Mozambique, which has the fifth highest prevalence in the world. Sussundenga District in Manica Province has documented high P. falciparum incidence at the local rural health center (RHC). This study’s objective was to analyze the P. falciparum temporal variation and model its pattern in Sussundenga District, Mozambique. Data from weekly epidemiological bulletins (BES) was collected from 2015 to 2019 and a time-series analysis was applied. For temporal modeling, a Box-Jenkins method was used with an autoregressive integrated moving average (ARIMA). Over the study period, 372,498 cases of P. falciparum were recorded in Sussundenga. There were weekly and yearly variations in incidence overall (p < 0.001). Children under five years had decreased malaria tendency, while patients over five years had an increased tendency. The ARIMA (2,2,1) (1,1,1) 52 model presented the least Root Mean Square being the most appropriate for forecasting. The goodness of fit was 68.15% for malaria patients less than five years old and 73.2% for malaria patients over five years old. The findings indicate that cases are decreasing among individuals less than five years and are increasing slightly in those older than five years. The P. falciparum case occurrence has a weekly temporal pattern peaking during the wet season. Based on the spatial and temporal distribution using ARIMA modelling, more efficient strategies that target this seasonality can be implemented to reduce the overall malaria burden in both Sussundenga District and regionally.

Predicting Diabetes and Estimating Its Economic Burden in China Using Autoregressive Integrated Moving Average Model

“…Therefore, the model can be used to predict the number of people with diabetes in China. In previous studies, the ARIMA model was commonly used to study the trend of infectious diseases such as the Corona Virus Disease 2019 (COVID-19) [ 36 ], tuberculosis [ 37 ], and malaria [ 38 ]. In this study, the raw data of diabetes can be transformed into a stationary time series after differencing, so we choose ARIMA model to fit and predict the prevalence trend of diabetes in China.…”

Long-lasting household damage from Cyclone Idai increases malaria risk in rural western Mozambique

“…1). Sussundenga village is the district municipality of Sussundenga District and is a rural agrarian community with an approximate population of 19,112 residents and shares a border with Zimbabwe 51,52 . From September to March, Manica Province has high temperatures and increased rainfall which contributes to the reported seasonally high malaria incidence.…”

Environmental predictors of malaria infection in Sussundenga, Mozambique

“…6 Ecologically, Sussundenga District has a signi cantly lower elevation compared to the Zimbabwe border and a more tropical climate compared to southern and northern Mozambique due to high seasonal rainfall. 7,17 The rainy season occurs from November to April. The annual precipitation is 1200mm and the average temperature is 21.2 °C.…”

“…The annual precipitation is 1200mm and the average temperature is 21.2 °C. 7 Data collection Publicly available climate data was collected from the United States Geological Survey's (USGS) Moderate Resolution Imaging Spectroradiometer (MODIS) and the NASA Digital Elevation Model (NASADEM) on day and night land surface temperature, evapotranspiration, normalized difference vegetation index (NDVI), land cover, and elevation.…”

“…3 In Mozambique, seasonal peaks range from December through April depending on the ecological zone and length of the rainy season. [3][4][5][6][7] There is high malaria prevalence in areas where climatic conditions are favorable to its development and transmission, whereas some drier parts of the country are more prone to epidemics. 3,4 Malaria transmission is in uenced by numerous environmental and ecological conditions.…”