Categories
malaria OMaWa

Validating the malaria model

A dynamic model of some malaria-transmitting anopheline mosquitoes of the Afrotropical region. II. Validation of species distribution and seasonal variations

Background
The first part of this study aimed to develop a model for Anopheles gambiae s.l. with separate parametrization schemes for Anopheles gambiae s.s. and Anopheles arabiensis. The characterizations were constructed based on literature from the past decades. This part of the study is focusing on the model’s ability to separate the mean state of the two species of the An. gambiae complex in Africa. The model is also evaluated with respect to capturing the temporal variability of An. arabiensis in Ethiopia. Before conclusions and guidance based on models can be made, models need to be validated.

Methods
The model used in this paper is described in part one (Malaria Journal 2013, 12:28). For the validation of the model, a data base of 5,935 points on the presence of An. gambiae s.s. and An. arabiensis was constructed. An additional 992 points were collected on the presence An. gambiae s.l.. These data were used to assess if the model could recreate the spatial distribution of the two species. The dataset is made available in the public domain. This is followed by a case study from Madagascar where the model’s ability to recreate the relative fraction of each species is investigated. In the last section the model’s ability to reproduce the temporal variability of An. arabiensis in Ethiopia is tested. The model was compared with data from four papers, and one field survey covering two years.

Results
Overall, the model has a realistic representation of seasonal and year to year variability in mosquito densities in Ethiopia. The model is also able to describe the distribution of An. gambiae s.s. and An. arabiensis in sub-Saharan Africa. This implies this model can be used for seasonal and long term predictions of changes in the burden of malaria. Before models can be used to improving human health, or guide which interventions are to be applied where, there is a need to understand the system of interest. Validation is an important part of this process. It is also found that one of the main mechanisms separating An. gambiae s.s. and An. arabiensis is the availability of hosts; humans and cattle. Climate play a secondary, but still important, role.

Malaria Journal 2013, 12:78

Categories
Climate malaria OMaWa

Using climate models in malaria models

Weather is important for transmission of malaria. The simplest way to measure the current weather is to walk outside and feel whether it is cold or hot, and see if it is sunny, cloudy, or rainy. Weather and climate models always start with a measurement of the current weather. Rather than relying on the perceived conditions, the weather models need accurate observations of the current weather. Such observations can be derived from satellites measuring the temperature, pressure, winds, and vapour content of the atmosphere. In addition, weather balloons can be used to correct the satellite derived observations. Once the current weather has been measured, these observations can be used as initial conditions in climate and weather models, and weather can be foretasted up to ten days ahead. With additional information about the temperature, density and salinity of the ocean, weather can be projected a season ahead. If you add volcanoes, variability of the sun, and greenhouse gasses, it is possible to estimate historical and future weather and climate.

 

Independent on which time period is of interest, these forecasts, or projections, have to be cover the entire Earth, and the forecasts involves solving millions of equations which demands computational resources. To keep the cost down, the global models are often run with a coarse resolution, dividing the Earth into squares of 250 km x 250 km. In regions where the land is flat for several thousand kilometres, and there is no ocean nearby, these coarse resolutions may be good enough to simulate the weather. Once a mountain, coastline, different land use, or soil types are present, the coarse global models represents the weather poorly. To aid this problem, one can use regional climate models (RCMs).

 

Regional climate models work by increasing the resolution of the global models in a smaller area, a domain, of interest. Such domain might cover southern Africa, Ethiopia, or western Africa, and resolve the terrain down to one by on km. The global climate model determines the large scale winds, temperatures, pressure, and humidity entering the smaller domain. The regional climate model can then resolve the local impact on weather from land use, orography, soil types etc., giving weather and climate information at much finer resolutions that what is feasible using a global model.

Categories
malaria OMaWa

Malaria model has been published

A dynamic model of some malaria-transmitting anopheline mosquitoes of the Afrotropical region. I. Model description and sensitivity analysis

Background
Most of the current biophysical models designed to address the large-scale distribution of malaria assume that transmission of the disease is independent of the vector involved. Another common assumption in these type of model is that the mortality rate of mosquitoes is constant over their life span and that their dispersion is negligible. Mosquito models are important in the prediction of malaria and hence there is a need for a realistic representation of the vectors involved.
Results
We construct a biophysical model including two competing species, Anopheles gambiae s.s. and Anopheles arabiensis. Sensitivity analysis highlight the importance of relative humidity and mosquito size, the initial conditions and dispersion, and a rarely used parameter, the probability of finding blood. We also show that the assumption of exponential mortality of adult mosquitoes does not match the observed data, and suggest that an age dimension can overcome this problem.
Conclusions
This study highlights some of the assumptions commonly used when constructing mosquito-malaria models and presents a realistic model of An. gambiae s.s. and An. arabiensis and their interaction. This new mosquito model, OMaWa, can improve our understanding of the dynamics of these vectors, which in turn can be used to understand the dynamics of malaria.

 

Malaria Journal 2013, 12:28

A dynamic model of some malaria-transmitting anopheline mosquitoes of the Afrotropical region. I. Model description and sensitivity analysis

Categories
Climate malaria

Fifteen years of erroneous malaria maps?

It is well known that the transmission of malaria is dependent on temperature, and of particular importance is how temperature controls the longevity of mosquitoes of the Anopheles genus. The temperature driven survival of these mosquitoes, is one of the main causes of the current global distribution of malaria.

The relationship between temperature and malaria transmission has made it possible to construct climate based malaria maps of the theoretical distribution of malaria, dividing severely affected areas, from areas with occasional epidemics. The same methodologies have also been used to project the future impact of climate change on malaria.

In 2013 two scientific papers were published describing how many of these models have been wrong headed. One by Mordecai and colleges, and one by our research group at Centre for International Health and Bjerknes Centre for Climate Research.Both papers claim malaria is transmitted most efficiently around 25 degrees Celsius, 6 °C lower than previous models. Roughly this means places with temperatures below or over 25 °C theoretically will have a lower potential for efficient spread of malaria compared to areas where temperatures are around 25 °C.

To describe the relationship between temperature and mosquito mortality, researchers use theoretical models. One model has been particular popular among scientists, and has been used in for example MARA/ARMA model used by the World Health Organization, to model the global constraints of malaria , and to project the consequences of global warming on malaria here, and here. In our paper we show that this model is not supported by observations, and there are reasons to believe that either the results shown in the mentioned papers are correct for the wrong reasons, or that the results themselves are wrong.

Researchers should now rethink how climate has influenced malaria in the past, present and future.