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 OMaWa Uncategorized

Malaria and Temperature

How malaria models relate temperature to malaria transmission

 

Background

It is well known that temperature has a major influence on the transmission of malaria parasites to their hosts. However, mathematical models do not always agree about the way in which temperature affects malaria transmission.

Methods

In this study, we compared six temperature dependent mortality models for the malaria vector Anopheles gambiae sensu stricto. The evaluation is based on a comparison between the models, and observations from semi-field and laboratory settings.

Results

Our results show how different mortality calculations can influence the predicted dynamics of malaria transmission.

Conclusions

With global warming a reality, the projected changes in malaria transmission will depend on which mortality model is used to make such predictions.

 

Parasites & Vectors 2013, 6:20

Categories
OMaWa

What functions should be implemented in OMaWa?

OMaWa is a climate driven malaria prediction system. In addition has the ability to be forced with other variables such as preventive measures (bed nets etc). This means that the model might be run for larger areas with only climate data as forcing, or it should be possible to run the model on a smaller scale and make use of information on bed net coverage, house spraying, mosquito resistance to pesticides etc.

So far the modelling system can read “pure” data, such as txt/csv files, NetCDF and partially supports GRIB (linux). In addition it supports ESRI shapefiles, and other GIS formats via helper functions. The GRIB format has been the hardest part to get working, and because of the variety of variants of this format. One thing is to read the data. Another thing is to read it into a standardized format usable for OMaWa (merging several files into one file). GRIB support is not the main focus as NetCDF probably is more common. After the data has been read, it is possible to select the sub-region of interest. The sub region could be an area, or a point. If the spatial resolution on the input parameters is high the point would also be an area to include what is taking place around the point.

In addition the climate parameters has to be converted to meaningful data for the model. OMaWa provides a way to prepare the data to force the model. The work flow would be something like:

  1. Read data (and convert/interpolate to right projection)
  2. Select time
  3. Select a region/point
  4. Calculate parameters/prepare data
  5. Set starting values (could be calculated)
  6. Run the model

To summarize; there are many functions that has to written to complete the steps to run the model. At the moment it is possible follow the steps above. Still there remain a lot of work to document, and streamline the work flow.