Modelled and observed mean and seasonal relationships between climate, population density and malaria indicators in Cameroon

Abstract

BackgroundA major health burden in Cameroon is malaria, a disease that is sensitive to climate, environment and socio-economic conditions, but whose precise relationship with these drivers is still uncertain. An improved understanding of the relationship between the disease and its drivers, and the ability to represent these relationships in dynamic disease models, would allow such models to contribute to health mitigation and adaptation planning. This work collects surveys of malaria parasite ratio and entomological inoculation rate and examines their relationship with temperature, rainfall, population density in Cameroon and uses this analysis to evaluate a climate sensitive mathematical model of malaria transmission.MethodsCo-located, climate and population data is compared to the results of 103 surveys of parasite ratio (PR) covering 18,011 people in Cameroon. A limited set of campaigns which collected year-long field-surveys of the entomological inoculation rate (EIR) are examined to determine the seasonality of disease transmission, three of the study locations are close to the Sanaga and Mefou rivers while others are not close to any permanent water feature. Climate-driven simulations of the VECTRI malaria model are evaluated with this analysis.ResultsThe analysis of the model results shows the PR peaking at temperatures of approximately 22 °C to 26 °C, in line with recent work that has suggested a cooler peak temperature relative to the established literature, and at precipitation rates at 7 mm day−1, somewhat higher than earlier estimates. The malaria model is able to reproduce this broad behaviour, although the peak occurs at slightly higher temperatures than observed, while the PR peaks at a much lower rainfall rate of 2 mm day−1. Transmission tends to be high in rural and peri-urban relative to urban centres in both model and observations, although the model is oversensitive to population which could be due to the neglect of population movements, and differences in hydrological conditions, housing quality and access to healthcare. The EIR follows the seasonal rainfall with a lag of 1 to 2 months, and is well reproduced by the model, while in three locations near permanent rivers the annual cycle of malaria transmission is out of phase with rainfall and the model fails.ConclusionMalaria prevalence is maximum at temperatures of 24 to 26 °C in Cameroon and rainfall rates of approximately 4 to 6 mm day−1. The broad relationships are reproduced in a malaria model although prevalence is highest at a lower rainfall maximum of 2 mm day−1. In locations far from water bodies malaria transmission seasonality closely follows that of rainfall with a lag of 1 to 2 months, also reproduced by the model, but in locations close to a seasonal river the seasonality of malaria transmission is reversed due to pooling in the transmission to the dry season, which the model fails to capture.

Malaria Risk Map Using Spatial Multi-Criteria Decision Analysis along Yunnan Border During the Pre-elimination Period

“… 49 In a mathematical model study, location far from water bodies was associated with malaria transmission seasonality, closely following rainfall, with a lag of 1–2 months. 75 A study carried out in China indicated that populations living within 60 m of water bodies had a higher risk of contracting malaria. 12 Based on studies indicating that mosquitoes fly no more than 170 m after ingesting a blood meal 76 and that a hungry mosquito will fly up to 1,500 m, 77 in this study areas above 1,500 m from water bodies were classified as low-risk areas.…”

“… 95 – 98 A study in Cameroon evaluating a mathematical model of malaria transmission noted that besides climatic factors, transmission tends to be high in rural and peri-urban areas relative to urban centers; however, one should be cautious that the model might be oversensitive by neglecting population movements and differences in hydrological conditions, housing quality, and access to health care. 75 In the model developed in this study, the assumption was set for the lowest risk of malaria infection when the distance away from central urban area was within 5,000 m.…”

Climate Variability and the Emergence of Malaria: Case of Kumbo Central Sub-Division, North West Region, Cameroon

“…Similarly, climate variability and change have aggravated morbidity and mortality resulting from malaria in most parts of Cameroon, particularly North West region. (Tchuinkam et al, 2010;Ndoforet al, 2013;Mbouna et al, 2019).…”

“…Malaria is endemic throughout Cameroon affecting almost 40% of its estimated 30 million people thus, constituting the first major cause of morbidity and mortality among the most vulnerable groups (children <5 years, pregnant women, people living with pre-existing medical conditions like HIV/AIDS and people living in abject poverty (Ngum et al, 2010;Mbouna et al, 2019). For instance, more than 50 percent of malaria morbidity occur in children below five years.…”

“…As a results those in need are left behind. With regards to the epidemiological profile and the transmission pattern, the western high plateau section is an endemic and perennial zone of consistent malaria transmission of between seven to twelve months (Ngum et al, 2010;Nkuo-Akenji et al, 2006;Tchuinkam et al, 2010;Tonui et al, 2008;Mbouna et al, 2019). This zone has a typical tropical climate with a prolonged rainy season of about eight months and a dry season of about four months.…”