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Teklehaimanot, Hailay D; Schwartz, Joel; Teklehaimanot, Awash; Lipsitch, Marc

doi:10.1186/1475-2875-3-44

Cited by 77 publications

(44 citation statements)

References 30 publications

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“…Despite the significance of weather variables in seasonal patterns of malaria, this analysis was not able to provide any evidence of a link between weather anomalies and unusually high incidence of malaria (e g, unseasonably high rainfall leading to a peak in malaria incidence), which is in line with previous findings [12,31]. This may indicate that unusually high incidence of malaria is not driven by unusual weather patterns in the counties, and rather, is mediated by other factors such as human behaviour, movement patterns, migration, or agricultural practices.…”

Section: Discussionsupporting

confidence: 87%

Plasmodium vivax malaria incidence over time and its association with temperature and rainfall in four counties of Yunnan Province, China

Wardrop

Barnett

Atkinson

et al. 2013

Malar J

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BackgroundTransmission of Plasmodium vivax malaria is dependent on vector availability, biting rates and parasite development. In turn, each of these is influenced by climatic conditions. Correlations have previously been detected between seasonal rainfall, temperature and malaria incidence patterns in various settings. An understanding of seasonal patterns of malaria, and their weather drivers, can provide vital information for control and elimination activities. This research aimed to describe temporal patterns in malaria, rainfall and temperature, and to examine the relationships between these variables within four counties of Yunnan Province, China.MethodsPlasmodium vivax malaria surveillance data (1991–2006), and average monthly temperature and rainfall were acquired. Seasonal trend decomposition was used to examine secular trends and seasonal patterns in malaria. Distributed lag non-linear models were used to estimate the weather drivers of malaria seasonality, including the lag periods between weather conditions and malaria incidence.ResultsThere was a declining trend in malaria incidence in all four counties. Increasing temperature resulted in increased malaria risk in all four areas and increasing rainfall resulted in increased malaria risk in one area and decreased malaria risk in one area. The lag times for these associations varied between areas.ConclusionsThe differences detected between the four counties highlight the need for local understanding of seasonal patterns of malaria and its climatic drivers.

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Section: Discussionsupporting

confidence: 87%

Plasmodium vivax malaria incidence over time and its association with temperature and rainfall in four counties of Yunnan Province, China

Wardrop

Barnett

Atkinson

et al. 2013

Malar J

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show abstract

“…On the other hand, higher maximum temperature seemed to have a negative effect on mosquito density. Maximum temperature could also influence mosquito survival and may result in breeding sites drying up faster after seasonal rains in cold environments such as the current study area [37]. …”

Section: Discussionmentioning

confidence: 99%

The effect of dams and seasons on malaria incidence and anopheles abundance in Ethiopia

et al. 2013

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BackgroundReservoirs created by damming rivers are often believed to increase malaria incidence risk and/or stretch the period of malaria transmission. In this paper, we report the effects of a mega hydropower dam on P. falciparum malaria incidence in Ethiopia.MethodsA longitudinal cohort study was conducted over a period of 2 years to determine Plasmodium falciparum malaria incidence among children less than 10 years of age living near a mega hydropower dam in Ethiopia. A total of 2080 children from 16 villages located at different distances from a hydropower dam were followed up from 2008 to 2010 using active detection of cases based on weekly house to house visits. Of this cohort of children, 951 (48.09%) were females and 1059 (51.91%) were males, with a median age of 5 years. Malaria vectors were simultaneously surveyed in all the 16 study villages. Frailty models were used to explore associations between time-to-malaria and potential risk factors, whereas, mixed-effects Poisson regression models were used to assess the effect of different covariates on anopheline abundance.ResultsOverall, 548 (26.86%) children experienced at least one clinical malaria episode during the follow up period with mean incidence rate of 14.26 cases/1000 child-months at risk (95% CI: 12.16 - 16.36). P. falciparum malaria incidence showed no statistically significant association with distance from the dam reservoir (p = 0.32). However, P. falciparum incidence varied significantly between seasons (p < 0.01). The malaria vector, Anopheles arabiensis, was however more abundant in villages nearer to the dam reservoir.ConclusionsP. falciparum malaria incidence dynamics were more influenced by seasonal drivers than by the dam reservoir itself. The findings could have implications in timing optimal malaria control interventions and in developing an early warning system in Ethiopia.

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“…It has been suggested that the major driving force of malaria transmission is climate [4-8]. However, the data has been largely inconsistent as to exactly how climate influences malaria transmission.…”

Section: Introductionmentioning

confidence: 99%

Model variations in predicting incidence of Plasmodium falciparum malaria using 1998-2007 morbidity and meteorological data from south Ethiopia

Loha

Lindtjørn

2010

Malar J

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BackgroundMalaria transmission is complex and is believed to be associated with local climate changes. However, simple attempts to extrapolate malaria incidence rates from averaged regional meteorological conditions have proven unsuccessful. Therefore, the objective of this study was to determine if variations in specific meteorological factors are able to consistently predict P. falciparum malaria incidence at different locations in south Ethiopia.MethodsRetrospective data from 42 locations were collected including P. falciparum malaria incidence for the period of 1998-2007 and meteorological variables such as monthly rainfall (all locations), temperature (17 locations), and relative humidity (three locations). Thirty-five data sets qualified for the analysis. Ljung-Box Q statistics was used for model diagnosis, and R squared or stationary R squared was taken as goodness of fit measure. Time series modelling was carried out using Transfer Function (TF) models and univariate auto-regressive integrated moving average (ARIMA) when there was no significant predictor meteorological variable.ResultsOf 35 models, five were discarded because of the significant value of Ljung-Box Q statistics. Past P. falciparum malaria incidence alone (17 locations) or when coupled with meteorological variables (four locations) was able to predict P. falciparum malaria incidence within statistical significance. All seasonal AIRMA orders were from locations at altitudes above 1742 m. Monthly rainfall, minimum and maximum temperature was able to predict incidence at four, five and two locations, respectively. In contrast, relative humidity was not able to predict P. falciparum malaria incidence. The R squared values for the models ranged from 16% to 97%, with the exception of one model which had a negative value. Models with seasonal ARIMA orders were found to perform better. However, the models for predicting P. falciparum malaria incidence varied from location to location, and among lagged effects, data transformation forms, ARIMA and TF orders.ConclusionsThis study describes P. falciparum malaria incidence models linked with meteorological data. Variability in the models was principally attributed to regional differences, and a single model was not found that fits all locations. Past P. falciparum malaria incidence appeared to be a superior predictor than meteorology. Future efforts in malaria modelling may benefit from inclusion of non-meteorological factors.

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Cited by 77 publications

References 30 publications

Plasmodium vivax malaria incidence over time and its association with temperature and rainfall in four counties of Yunnan Province, China

Plasmodium vivax malaria incidence over time and its association with temperature and rainfall in four counties of Yunnan Province, China

The effect of dams and seasons on malaria incidence and anopheles abundance in Ethiopia

Model variations in predicting incidence of Plasmodium falciparum malaria using 1998-2007 morbidity and meteorological data from south Ethiopia

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