Research

Current research

The School has a long history of research on infectious disease modelling, including the work of George Macdonald on malaria and schistosomiasis. There are several current projects involving infectious disease modelling, involving staff of all three of the School's departments, the Health Protection Agency and other collaborators. A particular strength of this research is that most of the modelling work benefits from the availability of extensive empirical data from the School's many epidemiological field studies.

Areas of research

• HIV
• Malaria
• Tuberculosis
• STIs and sexual networks
• Measles
• Avian Influenza
• Hookworm
• Human Papillomavirus
• Sleeping sickness
• Dengue
• Decision analytical models in health economics
• Decision analytical models in environmental health
• Mortality Estimation in Crisis-Affected Populations

HIV 

We have a number of projects that focus on understanding the spread and control of STI/HIV in developing countries.

We are using network and compartmental models to understand the spread and control of HIV in Uganda. Until now, Uganda has been successful in controlling its HIV epidemic, with falling HIV prevalence and incidence rates. However, recent data indicate that this decline is not continuing. HIV incidence has stabilised and HIV prevalence may be rising again. New efforts are therefore needed to build on past successes and identify the most effective HIV control strategies for the future. We are using mathematical models fitted to detailed empirical datasets from Uganda and elsewhere. The fitted models will be used to investigate the impact of a wide range of interventions on HIV incidence.

We are also using mathematical models to better understand what types of intervention would reduce HIV incidence among young people, a key Millenium Development Goal. We will fit mathematical models to detailed empirical datasets on adolescent sexual health from Tanzania, Zimbabwe, South Africa and Uganda. These include data from observational cohorts, randomised trials and other intervention studies. The fitted models will be used to investigate the range of factors that put young people and especially young women at risk, how these factors vary between populations with differing HIV epidemics and the potential impact of interventions targeted at adolescents.

In the HIVTools Research Group within the Centre, we have developed and use a wide range of simple to complex deterministic models to explore a range of policy-related questions including investigation of the impact and cost-effectiveness of a variety of HIV prevention interventions in different settings in Africa (South Africa, Benin, Malawi, Tanzania and Uganda), Asia (India, Bangladesh, Pakistan and China) and Central Europe (Ukraine and Belarus); the potential importance of new HIV prevention technologies and their characteristics (such as microbicides, HSV suppressive and episodic therapy, HIV vaccines and rapid STI diagnostics), and analysis to explore the generalisability of findings across different epidemiological settings. We focus on the use of detailed setting-specific behavioural and epidemiological data, and specialise in the use of different methods for incorporating data and model uncertainty into model projections. Currently we are conducting modelling related to the microbicide effectiveness trials and potential future introduction strategies, and evaluating the impact and cost-effectiveness of a large-scale multisite HIV/STI prevention intervention across southern India (Avahan). We also have many years experience in modelling the transmission of HIV and other blood borne viruses amongst injecting drug users, and have published numerous papers looking at the important factors that determine the impact of interventions focussing on this risk group. We are also estimating the clinical- and cost-effectiveness of the use of antiretroviral therapy for HIV with and without viral load / CD4 count monitoring in resource limited settings.

We are also estimating the clinical- and cost-effectiveness of the use of antiretroviral therapy for HIV with and without viral load / CD4 count monitoring in resource limited settings.

LSHTM researchers also run a collaborative network of HIV cohort studies, the Alpha Network which is being use to parameterise models of HIV epidemic spread and the demographic impact of HIV.

Malaria 

Infection with Plasmodium falciparum is a major cause of human morbidity and mortality, particularly among young children in Africa. There is currently no single effective measure (e.g., chemotherapy, vaccination, self protection, vector control) to control transmission. The clinical and epidemiological situation has deteriorated in recent decades due to a variety of factors, including socio-economic changes and antimalarial treatment becoming ineffective amid emergence of drug resistance. In addition, hopes of developing an effective preventive vaccine have not been easy to realise, and bednets are partially effective but coverage is still low in many endemic areas.

Crucial aspects in the persistence of malaria are the ability of the parasite to constantly evolve and adapt to changes in its environment, thus escaping some of the host's immune response and drug treatment, and the development of asymptomatic infection. Parasite genetic diversity and host heterogeneities are therefore important. The complexities of the parasite's life cycle, however, offer not only difficulties but also opportunities for developing new control measures. We use modelling to understand the epidemiology of malaria under various transmission settings (e.g. Africa, with generally high transmission intensity, and Asia, with lower transmission intensity and wider treatment availability) and the impact of promising new interventions, such as drug combinations and intermittent preventive treatment, as well as to assess the conditions for efficacy of different control measures.

We are also modelling the population structure, natural selection and spatial spread of drug resistance genes in Plasmodium populations. These biological models have been combined with economic models of the burden of drug-resistant malaria, to investigate the relative cost-effectiveness of alternative drug policies (eg. the replacement of current first line drugs with artemisin-based combination therapies). Population genetic models have also been developed to investigate the role of compliance to treatment regimen in the emergence and spread of drug resistance

We have also been developing models of the transmission of drug-resistant malaria, and of the likely effects on drug resistance of intermittent preventive therapy. This work has focused on the genotypes of gametocytes and oocysts, in order to quantify the effects of selective pressure exerted by treatment regimens being trailed in The Gambia, Tanzania and Pakistan.

In recent work we have been developing theoretical frameworks to examine the role of domestic animals, particularly cattle, in the epidemiology of malaria. These models have been validated against field data and used to evaluate the relative effectiveness of potential veterinary interventions aimed at controlling malaria under different epidemiological and economic settings.

Tuberculosis 

Considerable progress has been made during the last decade towards improving tuberculosis (TB) control using the DOTS strategy. DOTS aims to reduce the number of secondary M. tuberculosis infections generated through prompt diagnosis and effective treatment of symptomatic. Passive case-finding and treatment has reduced disease burden and improved TB treatment outcomes in countries with low HIV prevalence rates. At the global level, however, deteriorating control in Africa, primarily due to HIV, currently outweighs the gains being made in other regions. In collaboration with colleagues from the Gates-funded CREATE programme, we are developing mathematical models to evaluate alternative strategies that go beyond the DOTS, including isoniazid preventive therapy, more effective case-finding and antiretroviral therapy.

Also, in collaboration with Emilia Vynnycky at the Health Protection Agency we are working on models to integrate data on the molecular epidemiology of tuberculosis with those on the interaction of tuberculosis and HIV. These will be applied to the extensive long-term dataset from the School's research programme in Malawi. The work should help to further our understanding of the effect of HIV on the risk of developing tuberculosis soon after M tuberculosis (re)infection or through reactivation, and help provide improved insight into the impact of different interventions against tuberculosis.

STIs and sexual networks 

We are applying mathematical models of transmission dynamics to understand the spread of antibiotic resistant Neisseria gonorrhoeae with the aim to use such models to understand how resistant strains of gonorrhoea become established within the United Kingdom, as well as what control measures would be most effective in managing the resistance.

Measles 

We are using mathematical modelling to investigate the likely impact of HIV on measles control and its implication for vaccination policy in sittings with a high prevalence of HIV and endemic measles. This work is being done in collaboration with staff at the Health Protection Agency.

Avian influenza 

In collaboration with the Pasteur Institute in Cambodia (Dr Sirenda Vong), we are looking at the potential for zoonotic transfer of H5N1 from domestic poultry to humans in the home and at live-merchant markets.

Hookworm 

Hookworm infection is one of the most important parasitic infections of humans, causing anemia and malnutrition. The Human Hookworm Vaccine Initiative (HHVI) has identified and produced several vaccine candidates from hookworms, the most promising being the Na-ASP-2. Phase 2 of the vaccine trial is currently in preparation. However, surprisingly few data exist on the biology and population dynamics of hookworm infection. We are using mathematical models, in collaboration with others in the School, to explore the possible outcomes of the planned trials, to evaluate the impact of a vaccine at the population level and to assess its cost-effectiveness.

Human Papillomavirus 

It is now known that cervical cancer is caused by the human papillomavirus(HPV). This virus is sexually transmitted and has a high prevalence in young women, although in most cases it regresses naturally without harm, it can potentially progress to cervical cancer. Two vaccines are now available that protect against some, but not all, HPV types. As the vaccine does not protect against all types of HPV it may still be necessary to screen vaccinated women for cervical cancer. Currently we are involved in collaborative work assessing both the effectiveness and cost-effectiveness of cervical cancer screening options in the context of vaccination in both developed and developing countries.

Sleeping sickness 

In collaboration with researchers at the University of Glasgow, we are analysing large individual and aggregate datasets from Medecins Sans Frontieres field programmes to control gambiense sleeping sickness. We seek to optimise screening-based control strategies against this neglected disease. Models of sleeping sickness transmission are being developed, and applied to the field data so as to improve estimates of key parameters such as disease duration, explore the possible contribution of non-lethal infections to transmission, determine the schedules of screening campaigns most likely to result in local elimination, and estimate the coverage of passive case detection in the MSF programmes using Markov chain Monte Carlo sampling.

Dengue 

We have modelled the non-linear of impact of a partial reduction in dengue force of infection on dengue disease incidence in Singapore and are now developing dynamic models of the impact of novel genetic control strategies aimed at controlling Aedes mosquito populations on the epidemiology of dengue fever.

Decision analytical models in health economics 

Through collaboration with the Centre for Health Economics at University of York, we have developed a stochastic mathematical programming framework to optimally allocate resources within and between multiple healthcare programmes subject to budgetary and other constraints. The framework takes into account variability and uncertainty in the models, solves the optimal allocation problem under variability and uncertainty, and calculates the expected value of acquiring additional information to resolve model uncertainties.

Decision analytical models in environmental health 

In a collaborative project (PUrE) with other UK Universities, we are developing an integrated decision-support framework comprising a suite of mathematical models and methods to conduct assessments of options for a more sustainable management of urban pollution. The framework integrates a series of mathematical models comprising models of the fate and transport of pollutants in the urban environment and models of the impact of exposure to pollutants on human and ecological health. A key methodological theme in the decision-support framework is the characterisation and propagation of uncertainty within the series of models. Multi-criteria decision analysis is used to compare different options under uncertainty.

Mortality Estimation in Crisis-Affected Populations 

We are validating a new method to furnish rapid and precise estimates of mortality over recent periods in populations affected by disasters or armed conflict. We are using individual-based models to predict the reliability of the method in short periods and small populations, by exploring the degree to which random oscillation in mortality influences observed mortality patterns. This is a collaboration between ITD/DCVBU and PHP/HPU.