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      Assessing Climate Driven Malaria Variability in Ghana Using a Regional Scale Dynamical Model

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      Climate
      MDPI AG

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          Understanding the link between malaria risk and climate.

          The incubation period for malaria parasites within the mosquito is exquisitely temperature-sensitive, so that temperature is a major determinant of malaria risk. Epidemiological models are increasingly used to guide allocation of disease control resources and to assess the likely impact of climate change on global malaria burdens. Temperature-based malaria transmission is generally incorporated into these models using mean monthly temperatures, yet temperatures fluctuate throughout the diurnal cycle. Here we use a thermodynamic malaria development model to demonstrate that temperature fluctuation can substantially alter the incubation period of the parasite, and hence malaria transmission rates. We find that, in general, temperature fluctuation reduces the impact of increases in mean temperature. Diurnal temperature fluctuation around means >21 degrees C slows parasite development compared with constant temperatures, whereas fluctuation around <21 degrees C speeds development. Consequently, models which ignore diurnal variation overestimate malaria risk in warmer environments and underestimate risk in cooler environments. To illustrate the implications further, we explore the influence of diurnal temperature fluctuation on malaria transmission at a site in the Kenyan Highlands. Based on local meteorological data, we find that the annual epidemics of malaria at this site cannot be explained without invoking the influence of diurnal temperature fluctuation. Moreover, while temperature fluctuation reduces the relative influence of a subtle warming trend apparent over the last 20 years, it nonetheless makes the effects biologically more significant. Such effects of short-term temperature fluctuations have not previously been considered but are central to understanding current malaria transmission and the consequences of climate change.
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            A Climate-based Distribution Model of Malaria Transmission in Sub-Saharan Africa

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              Unexpected High Losses of Anopheles gambiae Larvae Due to Rainfall

              Background Immature stages of the malaria mosquito Anopheles gambiae experience high mortality, but its cause is poorly understood. Here we study the impact of rainfall, one of the abiotic factors to which the immatures are frequently exposed, on their mortality. Methodology/Principal Findings We show that rainfall significantly affected larval mosquitoes by flushing them out of their aquatic habitat and killing them. Outdoor experiments under natural conditions in Kenya revealed that the additional nightly loss of larvae caused by rainfall was on average 17.5% for the youngest (L1) larvae and 4.8% for the oldest (L4) larvae; an additional 10.5% (increase from 0.9 to 11.4%) of the L1 larvae and 3.3% (from 0.1 to 3.4%) of the L4 larvae were flushed away and larval mortality increased by 6.9% (from 4.6 to 11.5%) and 1.5% (from 4.1 to 5.6%) for L1 and L4 larvae, respectively, compared to nights without rain. On rainy nights, 1.3% and 0.7% of L1 and L4 larvae, respectively, were lost due to ejection from the breeding site. Conclusions/Significance This study demonstrates that immature populations of malaria mosquitoes suffer high losses during rainfall events. As these populations are likely to experience several rain showers during their lifespan, rainfall will have a profound effect on the productivity of mosquito breeding sites and, as a result, on the transmission of malaria. These findings are discussed in the light of malaria risk and changing rainfall patterns in response to climate change.
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                Author and article information

                Journal
                CLIMC9
                Climate
                Climate
                MDPI AG
                2225-1154
                March 2017
                March 05 2017
                : 5
                : 1
                : 20
                Article
                10.3390/cli5010020
                e7f78ee8-2091-4317-af83-7c0111932166
                © 2017

                https://creativecommons.org/licenses/by/4.0/

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