Development of a new version of the Liverpool Malaria Model. I. Refining the parameter settings and mathematical formulation of basic processes based on a literature review

Temperature-dependent pre-bloodmeal period and temperature-driven asynchrony between parasite development and mosquito biting rate reduce malaria transmission intensity

A mosquito needs to bite at least twice for malaria transmission to occur: once to acquire parasites and, after these parasites complete their development in their mosquito host, once to transmit the parasites to the next vertebrate host. Here we investigate the relationship between temperature, parasite development, and biting frequency in a mosquito-rodent malaria model system. We show that the pre-bloodmeal period (the time lag between mosquito emergence and first bloodmeal) increases at lower temperatures. In addition, parasite development time and feeding exhibit different thermal sensitivities such that mosquitoes might not be ready to feed at the point at which the parasite is ready to be transmitted. Exploring these effects using a simple theoretical model of human malaria shows that delays in infection and transmission can reduce the vectorial capacity of malaria mosquitoes by 20 to over 60%, depending on temperature. These delays have important implications for disease epidemiology and control, and should be considered in future transmission models.

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.

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.

Elimination of tropical disease through surveillance and response

Surveillance and response represent the final crucial steps in achieving effective control and particularly elimination of communicable diseases as recognized in the area of neglected tropical diseases (NTDs), applied in increasing numbers in endemic countries with ongoing control and elimination programmers. More and more national NTD elimination initiatives are scheduled based on the innovative and effective One world-One health perspective to detect pockets of transmission and disease reintroduction. Resource-constrained countries, which carry the heaviest NTD burdens, face various challenges how to strengthen the health system as well as developing effective and novel tools for surveillance and response tailored to local settings. Surveillance-response approaches take place in two different stages corralling the basic components of the surveillance-response system for NTD elimination. Six different research priorities have been identified:1)dynamic mapping of transmission, 2) near real-time capture of population dynamics, 3) modelling based on a minimum essential database/dataset, 4) implementation of mobile health (m-health) and sensitive diagnostics, 5) design of effective response packages tailored to different transmission settings and levels, and 6) validation of approaches and responses packages.

A model of malaria epidemiology involving weather, exposure and transmission applied to north East India

Background

Quantitative relations between weather variables and malaria vector can enable pro-active control through meteorological monitoring. Such relations are also critical for reliable projections in a changing climate, especially since the vector abundance depends on a combination of weather variables, each in a given range. Further, such models need to be region-specific as vector population and exposure depend on regional characteristics.

Methods

We consider days of genesis based on daily temperature, rainfall and humidity in given ranges. We define a single model parameter based on estimates of exposure and transmission to calibrate the model; the model is applied to 12 districts of Arunachal Pradesh, a region endemic to malaria. The epidemiological data is taken as blood samples that test positive. The meteorological data is adopted from NCEP daily Reanalysis on a global grid; population data is used to estimate exposure and transmission coefficients.

Results

The observed annual cycles (2006–2010) and the interannual variability (2002–2010) of epidemiology are well simulated for each of the 12 districts by the model. While no single weather variable like temperature can reproduce the observed epidemiology, a combination of temperature, rainfall and humidity provides an accurate description of the annual cycle as well as the inter annual variability over all the 12 districts.

Conclusion

Inclusion of the three meteorological variables, along with the expressions for exposure and transmission, can quite accurately represent observed epidemiology over multiple locations and different years. The model is potentially useful for outbreak forecasts at short time scales through high resolution weather monitoring; however, validation with longer and independent epidemiological data is required for more robust estimation of realizable skill. While the model has been examined over a specific region, the basic algorithm is easily applicable to other regions; the model can account for shifting vulnerability due to regional climate change.

Modeling the role of environmental variables on the population dynamics of the malaria vector Anopheles gambiae sensu stricto

Background

The impact of weather and climate on malaria transmission has attracted considerable attention in recent years, yet uncertainties around future disease trends under climate change remain. Mathematical models provide powerful tools for addressing such questions and understanding the implications for interventions and eradication strategies, but these require realistic modeling of the vector population dynamics and its response to environmental variables.

Methods

Published and unpublished field and experimental data are used to develop new formulations for modeling the relationships between key aspects of vector ecology and environmental variables. These relationships are integrated within a validated deterministic model of Anopheles gambiae s.s. population dynamics to provide a valuable tool for understanding vector response to biotic and abiotic variables.

Results

A novel, parsimonious framework for assessing the effects of rainfall, cloudiness, wind speed, desiccation, temperature, relative humidity and density-dependence on vector abundance is developed, allowing ease of construction, analysis, and integration into malaria transmission models. Model validation shows good agreement with longitudinal vector abundance data from Tanzania, suggesting that recent malaria reductions in certain areas of Africa could be due to changing environmental conditions affecting vector populations.

Conclusions

Mathematical models provide a powerful, explanatory means of understanding the role of environmental variables on mosquito populations and hence for predicting future malaria transmission under global change. The framework developed provides a valuable advance in this respect, but also highlights key research gaps that need to be resolved if we are to better understand future malaria risk in vulnerable communities.

Indo-Gangetic river systems, monsoon and malaria

The history of the Indo-Gangetic river systems from the late nineteenth to the early twentieth centuries can be reconstructed from the meticulous official records of the survey, meteorological and medical departments of the British Government of India. In contrast with the grand sweep of the geological evidence, these records indicate a complex narrative of floods, droughts and channel shifts. Similarly, the cumulative growth of the Ganges-Brahmaputra and Indus deltas was overprinted by the effects of the annual monsoon cycle on precipitation, temperature and winds. Malaria, the principal vector-borne disease of the Indian subcontinent, and the deadliest, displayed epidemiological types that ranged between the extremes of stable-endemic to unstable-epidemic as defined in the classic theory of equilibrium of George Macdonald. Variations in its transmission, incidence and prevalence were closely tied to the different deltaic environments of the Bengal and Indus basins and to the short-sightedness of many irrigation and related engineering schemes.

The impact of regional climate change on malaria risk due to greenhouse forcing and land-use changes in tropical Africa

BACKGROUND

Climate change will probably alter the spread and transmission intensity of malaria in Africa.

OBJECTIVES

In this study, we assessed potential changes in the malaria transmission via an integrated weather-disease model.

METHODS

We simulated mosquito biting rates using the Liverpool Malaria Model (LMM). The input data for the LMM were bias-corrected temperature and precipitation data from the regional model (REMO) on a 0.5° latitude-longitude grid. A Plasmodium falciparum infection model expands the LMM simulations to incorporate information on the infection rate among children. Malaria projections were carried out with this integrated weather-disease model for 2001 to 2050 according to two climate scenarios that include the effect of anthropogenic land-use and land-cover changes on climate.

RESULTS

Model-based estimates for the present climate (1960 to 2000) are consistent with observed data for the spread of malaria in Africa. In the model domain, the regions where malaria is epidemic are located in the Sahel as well as in various highland territories. A decreased spread of malaria over most parts of tropical Africa is projected because of simulated increased surface temperatures and a significant reduction in annual rainfall. However, the likelihood of malaria epidemics is projected to increase in the southern part of the Sahel. In most of East Africa, the intensity of malaria transmission is expected to increase. Projections indicate that highland areas that were formerly unsuitable for malaria will become epidemic, whereas in the lower-altitude regions of the East African highlands, epidemic risk will decrease.

CONCLUSIONS

We project that climate changes driven by greenhouse-gas and land-use changes will significantly affect the spread of malaria in tropical Africa well before 2050. The geographic distribution of areas where malaria is epidemic might have to be significantly altered in the coming decades.

Modelling the impact of vector control interventions on Anopheles gambiae population dynamics

Background

Intensive anti-malaria campaigns targeting the Anopheles population have demonstrated substantial reductions in adult mosquito density. Understanding the population dynamics of Anopheles mosquitoes throughout their whole lifecycle is important to assess the likely impact of vector control interventions alone and in combination as well as to aid the design of novel interventions.

Methods

An ecological model of Anopheles gambiae sensu lato populations incorporating a rainfall-dependent carrying capacity and density-dependent regulation of mosquito larvae in breeding sites is developed. The model is fitted to adult mosquito catch and rainfall data from 8 villages in the Garki District of Nigeria (the 'Garki Project') using Bayesian Markov Chain Monte Carlo methods and prior estimates of parameters derived from the literature. The model is used to compare the impact of vector control interventions directed against adult mosquito stages - long-lasting insecticide treated nets (LLIN), indoor residual spraying (IRS) - and directed against aquatic mosquito stages, alone and in combination on adult mosquito density.

Results

A model in which density-dependent regulation occurs in the larval stages via a linear association between larval density and larval death rates provided a good fit to seasonal adult mosquito catches. The effective mosquito reproduction number in the presence of density-dependent regulation is dependent on seasonal rainfall patterns and peaks at the start of the rainy season. In addition to killing adult mosquitoes during the extrinsic incubation period, LLINs and IRS also result in less eggs being oviposited in breeding sites leading to further reductions in adult mosquito density. Combining interventions such as the application of larvicidal or pupacidal agents that target the aquatic stages of the mosquito lifecycle with LLINs or IRS can lead to substantial reductions in adult mosquito density.

Conclusions

Density-dependent regulation of anopheline larvae in breeding sites ensures robust, stable mosquito populations that can persist in the face of intensive vector control interventions. Selecting combinations of interventions that target different stages in the vector's lifecycle will result in maximum reductions in mosquito density.

Development of a new version of the Liverpool Malaria Model. II. Calibration and validation for West Africa

Background

In the first part of this study, an extensive literature survey led to the construction of a new version of the Liverpool Malaria Model (LMM). A new set of parameter settings was provided and a new development of the mathematical formulation of important processes related to the vector population was performed within the LMM. In this part of the study, so far undetermined model parameters are calibrated through the use of data from field studies. The latter are also used to validate the new LMM version, which is furthermore compared against the original LMM version.

Methods

For the calibration and validation of the LMM, numerous entomological and parasitological field observations were gathered for West Africa. Continuous and quality-controlled temperature and precipitation time series were constructed using intermittent raw data from 34 weather stations across West Africa. The meteorological time series served as the LMM data input. The skill of LMM simulations was tested for 830 different sets of parameter settings of the undetermined LMM parameters. The model version with the highest skill score in terms of entomological malaria variables was taken as the final setting of the new LMM version.

Results

Validation of the new LMM version in West Africa revealed that the simulations compare well with entomological field observations. The new version reproduces realistic transmission rates and simulated malaria seasons are comparable to field observations. Overall the new model version performs much better than the original model. The new model version enables the detection of the epidemic malaria potential at fringes of endemic areas and, more importantly, it is now applicable to the vast area of malaria endemicity in the humid African tropics.

Conclusions

A review of entomological and parasitological data from West Africa enabled the construction of a new LMM version. This model version represents a significant step forward in the modelling of a weather-driven malaria transmission cycle. The LMM is now more suitable for the use in malaria early warning systems as well as for malaria projections based on climate change scenarios, both in epidemic and endemic malaria areas.