Olivier J. T. Briët

The effect of malaria control on Plasmodium falciparum in Africa between 2000 and 2015

Since the year 2000, a concerted campaign against malaria has led to unprecedented levels of intervention coverage across sub-Saharan Africa. Understanding the effect of this control effort is vital to inform future control planning. However, the effect of malaria interventions across the varied epidemiological settings of Africa remains poorly understood owing to the absence of reliable surveillance data and the simplistic approaches underlying current disease estimates. Here we link a large database of malaria field surveys with detailed reconstructions of changing intervention coverage to directly evaluate trends from 2000 to 2015, and quantify the attributable effect of malaria disease control efforts. We found that Plasmodium falciparum infection prevalence in endemic Africa halved and the incidence of clinical disease fell by 40% between 2000 and 2015. We estimate that interventions have averted 663 (542–753 credible interval) million clinical cases since 2000. Insecticide-treated nets, the most widespread intervention, were by far the largest contributor (68% of cases averted). Although still below target levels, current malaria interventions have substantially reduced malaria disease incidence across the continent. Increasing access to these interventions, and maintaining their effectiveness in the face of insecticide and drug resistance, should form a cornerstone of post-2015 control strategies.

Behavioural heterogeneity of Anopheles species in ecologically different localities in Southeast Asia: a challenge for vector control.

In Southeast Asia the biodiversity of Anopheles species in the domestic environment is very high. Only few species are considered major vectors throughout the region, whereas the vector status of other species varies from area to area. Often it is difficult to identify an Anopheles species as a malaria vector in areas with low malaria incidence. The behaviour of Anopheles species largely determines their vector status, and insights into their behaviour are essential to evaluate the appropriateness of vector control measures. This study was conducted in six ecologically different localities in Southeast Asia to rank the different Anopheles species in terms of anthropophily and endophagy in order to estimate their current epidemiological importance. Concurrently, the biting and resting behaviour of the vectors was analysed to evaluate the appropriateness of insecticide‐impregnated bed nets and residual house spraying in vector control. Anopheles dirus A was highly anthropophilic at all sites where it occurred. By contrast, the degree of anthropophily exhibited by An. minimus A depended on availability of cattle. Anopheles campestris, An. nimpe, An. sinensis, An. maculatus, An. aconitus showed a high degree of anthropophily in certain villages, indicating their potential of participating in malaria transmission, although the actual incidence of malaria in the study villages can be fully explained by transmission of the major vectors (An. dirus A, An. minimus A and An. sundaicus). Late biting of An. minimus A and biting activity throughout the night of An. sundaicus favour bed nets as a control method for these species, whilst exophilic and outdoor biting in combination with early feeding behaviour of An. dirus A will make both insecticide‐impregnated bed nets and indoor residual spraying less suitable for controlling this species. Spatial variation in biting and resting behaviour was observed within almost all Anopheles species. These heterogeneities may result in the differences in epidemiological importance and in response to vector control of Anopheles species in different areas. Moreover, environmental changes and changes in human practice are expected to influence the behaviour, hence the role of the different species in malaria transmission. The effect of environmental changes on vector behaviour should be followed up carefully.

Mapping malaria transmission in West and Central Africa

We have produced maps of Plasmodium falciparum malaria transmission in West and Central Africa using the Mapping Malaria Risk in Africa (MARA) database comprising all malaria prevalence surveys in these regions that could be geolocated. The 1846 malaria surveys analysed were carried out during different seasons, and were reported using different age groupings of the human population. To allow comparison between these, we used the Garki malaria transmission model to convert the malaria prevalence data at each of the 976 locations sampled to a single estimate of transmission intensity E, making use of a seasonality model based on Normalized Difference Vegetation Index (NDVI), temperature and rainfall data. We fitted a Bayesian geostatistical model to E using further environmental covariates and applied Bayesian kriging to obtain smooth maps of E and hence of age‐specific prevalence. The product is the first detailed empirical map of variations in malaria transmission intensity that includes Central Africa. It has been validated by expert opinion and in general confirms known patterns of malaria transmission, providing a baseline against which interventions such as insecticide‐treated nets programmes and trends in drug resistance can be evaluated. There is considerable geographical variation in the precision of the model estimates and, in some parts of West Africa, the predictions differ substantially from those of other risk maps. The consequent uncertainties indicate zones where further survey data are needed most urgently. Malaria risk maps based on compilations of heterogeneous survey data are highly sensitive to the analytical methodology.

Consistently high estimates for the proportion of human exposure to malaria vector populations occurring indoors in rural Africa

Background Insecticide-treated nets (ITNs) and indoor residual spraying (IRS) are highly effective tools for controlling malaria transmission in Africa because the most important vectors, from the Anopheles gambiae complex and the A. funestus group, usually prefer biting humans indoors at night. Methods Matched surveys of mosquito and human behaviour from six rural sites in Burkina Faso, Tanzania, Zambia, and Kenya, with ITN use ranging from 0.2% to 82.5%, were used to calculate the proportion of human exposure to An. gambiae sensu lato and An. funestus s.l. that occurs indoors (πi), as an indicator of the upper limit of personal protection that indoor vector control measures can provide. This quantity was also estimated through use of a simplified binary analysis (πiB) so that the proportions of mosquitoes caught indoors (Pi), and between the first and last hours at which most people are indoors (Pfl) could also be calculated as underlying indicators of feeding by mosquitoes indoors or at night, respectively. Results The vast majority of human exposure to Anopheles bites occurred indoors (πiB = 0.79–1.00). Neither An. gambiae s.l. nor An. funestus s.l. strongly preferred feeding indoors (Pi = 0.40–0.63 and 0.22–0.69, respectively), but they overwhelmingly preferred feeding at times when most humans were indoors (Pfl = 0.78–1.00 and 0.86–1.00, respectively). Conclusions These quantitative summaries of behavioural interactions between humans and mosquitoes constitute a remarkably consistent benchmark with which future observations of vector behaviour can be compared. Longitudinal monitoring of these quantities is vital to evaluate the effectiveness of ITNs and IRS and the need for complementary measures that target vectors outdoors.

Temporal correlation between malaria and rainfall in Sri Lanka

BackgroundRainfall data have potential use for malaria prediction. However, the relationship between rainfall and the number of malaria cases is indirect and complex.MethodsThe statistical relationships between monthly malaria case count data series and monthly mean rainfall series (extracted from interpolated station data) over the period 1972 – 2005 in districts in Sri Lanka was explored in four analyses: cross-correlation; cross-correlation with pre-whitening; inter-annual; and seasonal inter-annual regression.ResultsFor most districts, strong positive correlations were found for malaria time series lagging zero to three months behind rainfall, and negative correlations were found for malaria time series lagging four to nine months behind rainfall. However, analysis with pre-whitening showed that most of these correlations were spurious. Only for a few districts, weak positive (at lags zero and one) or weak negative (at lags two to six) correlations were found in pre-whitened series. Inter-annual analysis showed strong negative correlations between malaria and rainfall for a group of districts in the centre-west of the country. Seasonal inter-annual analysis showed that the effect of rainfall on malaria varied according to the season and geography.ConclusionSeasonally varying effects of rainfall on malaria case counts may explain weak overall cross-correlations found in pre-whitened series, and should be taken into account in malaria predictive models making use of rainfall as a covariate.

Human exposure to anopheline mosquitoes occurs primarily indoors, even for users of insecticide-treated nets in Luangwa Valley, south-east Zambia

BackgroundCurrent front line malaria vector control methods such as indoor residual spraying (IRS) and long-lasting insecticidal nets (LLINs), rely upon the preference of many primary vectors to feed and/or rest inside human habitations where they can be targeted with domestically-applied insecticidal products. We studied the human biting behaviour of the malaria vector Anopheles funestus Giles and the potential malaria vector Anopheles quadriannulatus Theobald in Luangwa valley, south-east Zambia.MethodsMosquitoes were collected by human landing catch in blocks of houses with either combined use of deltamethrin-based IRS and LLINs or LLINs alone. Human behaviour data were collected to estimate how much exposure to mosquito bites indoors and outdoors occurred at various times of the night for LLIN users and non-users.ResultsAnopheles funestus and An. quadriannulatus did not show preference to bite either indoors or outdoors: the proportions [95% confidence interval] caught indoors were 0.586 [0.303, 0.821] and 0.624 [0.324, 0.852], respectively. However, the overwhelming majority of both species were caught at times when most people are indoors. The proportion of mosquitoes caught at a time when most people are indoors were 0.981 [0.881, 0.997] and 0.897 [0.731, 0.965], respectively, so the proportion of human exposure to both species occuring indoors was high for individuals lacking LLINs (An. funestus: 0.983 and An. quadriannulatus: 0.970, respectively). While LLIN users were better protected, more than half of their exposure was nevertheless estimated to occur indoors (An. funestus: 0.570 and An. quadriannulatus: 0.584).ConclusionsThe proportion of human exposure to both An. funestus and An. quadriannulatus occuring indoors was high in the area and hence both species might be responsive to further peri-domestic measures if these mosquitoes are susceptible to insecticidal products.

Models for short term malaria prediction in Sri Lanka

BackgroundMalaria in Sri Lanka is unstable and fluctuates in intensity both spatially and temporally. Although the case counts are dwindling at present, given the past history of resurgence of outbreaks despite effective control measures, the control programmes have to stay prepared. The availability of long time series of monitored/diagnosed malaria cases allows for the study of forecasting models, with an aim to developing a forecasting system which could assist in the efficient allocation of resources for malaria control.MethodsExponentially weighted moving average models, autoregressive integrated moving average (ARIMA) models with seasonal components, and seasonal multiplicative autoregressive integrated moving average (SARIMA) models were compared on monthly time series of district malaria cases for their ability to predict the number of malaria cases one to four months ahead. The addition of covariates such as the number of malaria cases in neighbouring districts or rainfall were assessed for their ability to improve prediction of selected (seasonal) ARIMA models.ResultsThe best model for forecasting and the forecasting error varied strongly among the districts. The addition of rainfall as a covariate improved prediction of selected (seasonal) ARIMA models modestly in some districts but worsened prediction in other districts. Improvement by adding rainfall was more frequent at larger forecasting horizons.ConclusionHeterogeneity of patterns of malaria in Sri Lanka requires regionally specific prediction models. Prediction error was large at a minimum of 22% (for one of the districts) for one month ahead predictions. The modest improvement made in short term prediction by adding rainfall as a covariate to these prediction models may not be sufficient to merit investing in a forecasting system for which rainfall data are routinely processed.

Maps of the Sri Lanka malaria situation preceding the tsunami and key aspects to be considered in the emergency phase and beyond

BackgroundFollowing the tsunami, a detailed overview of the area specific transmission levels is essential in assessing the risk of malaria in Sri Lanka. Recent information on vector insecticide resistance, parasite drug resistance, and insights into the national policy for malaria diagnosis and treatment are important in assisting national and international agencies in their control efforts.MethodsMonthly records over the period January 1995 – October 2004 of confirmed malaria cases were used to perform an analysis of malaria distribution at district spatial resolution. Also, a focused review of published reports and routinely collected information was performed.ResultsThe incidence of malaria was only 1 case per thousand population in the 10 months leading up to the disaster, in the districts with the highest transmission.ConclusionAlthough relocated people may be more exposed to mosquito bites, and their capacity to handle diseases affected, the environmental changes caused by the tsunami are unlikely to enhance breeding of the principal vector, and, given the present low parasite reservoir, the likelihood of a malaria outbreak is low. However, close monitoring of the situation is necessary, especially as December – February is normally the peak transmission season. Despite some losses, the Sri Lanka public health system is capable of dealing with the possible threat of a malaria outbreak after the tsunami. The influx of foreign medical assistance, drugs, and insecticides may interfere with malaria surveillance, and the long term malaria control strategy of Sri Lanka, if not in accordance with government policy.

Sri Lanka malaria maps

BackgroundDespite a relatively good national case reporting system in Sri Lanka, detailed maps of malaria distribution have not been publicly available.MethodsIn this study, monthly records over the period 1995 – 2000 of microscopically confirmed malaria parasite positive blood film readings, at sub-district spatial resolution, were used to produce maps of malaria distribution across the island. Also, annual malaria trends at district resolution were displayed for the period 1995 – 2002.ResultsThe maps show that Plasmodium vivax malaria incidence has a marked variation in distribution over the island. The incidence of Plasmodium falciparum malaria follows a similar spatial pattern but is generally much lower than that of P. vivax. In the north, malaria shows one seasonal peak in the beginning of the year, whereas towards the south a second peak around June is more pronounced.ConclusionThis paper provides the first publicly available maps of both P. vivax and P. falciparum malaria incidence distribution on the island of Sri Lanka at sub-district resolution, which may be useful to health professionals, travellers and travel medicine professionals in their assessment of malaria risk in Sri Lanka. As incidence of malaria changes over time, regular updates of these maps are necessary.

Effects of pyrethroid resistance on the cost effectiveness of a mass distribution of long-lasting insecticidal nets: a modelling study

BackgroundThe effectiveness of insecticide-treated nets in preventing malaria is threatened by developing resistance against pyrethroids. Little is known about how strongly this affects the effectiveness of vector control programmes.MethodsData from experimental hut studies on the effects of long-lasting, insecticidal nets (LLINs) on nine anopheline mosquito populations, with varying levels of mortality in World Health Organization susceptibility tests, were used to parameterize malaria models. Both simple static models predicting population-level insecticidal effectiveness and protection against blood feeding, and complex dynamic epidemiological models, where LLINs decayed over time, were used. The epidemiological models, implemented in OpenMalaria, were employed to study the impact of a single mass distribution of LLINs on malaria, both in terms of episodes prevented during the effective lifetime of the batch of LLINs, and in terms of net health benefits (NHB) expressed in disability-adjusted life years (DALYs) averted during that period, depending on net type (standard pyrethroid-only LLIN or pyrethroid-piperonyl butoxide combination LLIN), resistance status, coverage and pre-intervention transmission level.ResultsThere were strong positive correlations between insecticide susceptibility status and predicted population level insecticidal effectiveness of and protection against blood feeding by LLIN intervention programmes. With the most resistant mosquito population, the LLIN mass distribution averted up to about 40% fewer episodes and DALYs during the effective lifetime of the batch than with fully susceptible populations. However, cost effectiveness of LLINs was more sensitive to the pre-intervention transmission level and coverage than to susceptibility status. For four out of the six Anopheles gambiae sensu lato populations where direct comparisons between standard LLINs and combination LLINs were possible, combination nets were more cost effective, despite being more expensive. With one resistant population, both net types were equally effective, and with one of the two susceptible populations, standard LLINs were more cost effective.ConclusionDespite being less effective when compared to areas with susceptible mosquito populations, standard and combination LLINs are likely to (still) be cost effective against malaria even in areas with strong pyrethroid resistance. Combination nets are likely to be more cost effective than standard nets in areas with resistant mosquito populations.