The relationship between plasmodium knowlesi malaria distribution and environmental factors in Kelantan - Ismail, Ku Mohd Saifullah Ku (2023)
Introduction: P. knowlesi malaria infection has emerged in Kelantan for the past two decades. Despite considerable preventive measures to combat P. knowlesi malaria infection, the prevalence of P. knowlesi malaria in Kelantan is not predicted to decline anytime soon. To improve control measures of P. knowlesi malaria infection in Kelantan, the assessment of the relationship between P. knowlesi malaria and the environment is important due to the impact of environmental factors, such as spatial, climatic, and land use changes, on the transmission dynamics of P. knowlesi malaria. Thus, this study aimed to develop the cumulative and annual spatial intensity map of notified P. knowlesi malaria infection in Kelantan, to estimate the relationship between meteorological indicators and the relative risk of P. knowlesi malaria in Kelantan using distributed lag non-linear analysis, and to predict the geographical distribution of P. knowlesi malaria incidence using data from e-Vekpro system from the year 2012 to year 2021 in Kelantan, Malaysia. Methods: The secondary data review was conducted using registered malaria cases in the e-Vekpro system from 2012 to 2021 via R software. The spatial intensity map of the cases was stratified by age group, sex, month, and year of diagnosis, and was estimated using kernel density estimation via 'spatstat' package. Six meteorological variables from Kuala Krai Weather Station, namely daily rainfall, daily mean temperature, daily minimum temperature, daily maximum temperature, daily mean surface wind speed and daily mean relative humidity were obtained from Malaysia Meteorological Department. A distributed lag non-linear model was used to examine the significant correlated meteorological parameters on the relative risk of P. knowlesi malaria infection in Kelantan. Bayesian geostatistical modeling based on integrated nested Laplace approximation and stochastic partial differential equation approach was used to map the predicted P. knowlesi malaria incidence in Kelantan with a 5 x 5 kilometers spatial resolution. This geostatistical approach used open-source continuous raster data as covariates from various sources, namely rainfall, mean temperature, temperature annual range, water vapor pressure, wind speed, elevation, forest height, and population density, representing environmental factors. Results: A total of 1014 cases were included in the study. Mapping of spatial intensity demonstrated that the interior area of Kelantan had a higher spatial intensity of P. knowlesi malaria infection. Spatial variation of case intensity demonstrated that cases among male were more scattered and dispersed towards Kelantan's western, southern, and eastern border. The cases aged between 20 to 49 years old were more abundant within Gua Musang district. Meanwhile, the other age groups tend to be clustered in Jeli and Kuala Krai districts. Temporal variation by month revealed that the cases become more abundant in Jeli, Kuala Krai, and Gua Musang districts from October to April, then Jeli districts in May, and Gua Musang districts from June to September. Temporal variation by year showed a shifting pattern of case intensity from center of Gua Musang towards the southern Kelantan border. The relationship between a meteorological variable and the number of P. knowlesi malaria cases reveals that the minimum temperature and weekly average rainfall were significantly and negatively associated with the number of P. knowlesi malaria cases. Weekly average rainfall below 4.2mm was linked to a higher relative risk of P. knowlesi malaria, while higher than 4.2mm was linked to a lower relative risk of P. knowlesi malaria up to 12 lag weeks. Weekly average minimum temperatures elow 23.4°C reduce the relative risk of P. knowlesi malaria, but temperatures over 23.4°C raise that risk within 12 lag weeks. Geostatistical analysis shows spatial heterogeneity of predicted geographical distribution of P. knowlesi malaria infection was prominent over the central western part of the Gua Musang district and spread toward the southern border of Kelantan. Several areas in Gua Musang District have predicted geographical incidence of P. knowlesi malaria higher than 450 cases per 10000 population, which is approximately 3.9 times higher than the observed incidence. The exceedance probability suggested that several areas in the Gua Musang district have a higher chance of predicted P. knowlesi malaria infection than 100 cases per 10000 population. Conclusion: Environmental factors, including meteorology, topography, and land cover, have an impact on the relative risk and incidence of P. knowlesi malaria in Kelantan. This may provide additional information for a more innovative and strategic intervention planning, optimizing funding allocation and human resources, as well as multisectoral approach in P. knowlesi malaria prevention and control specifically in Kelantan.