A model is a representation of a feature (or part of it) in a form that is easier to understand, define, quantify, visualize or simulate by referencing it to an existing and usually common accepted knowledge. This work package will utilize mathematical models to predict and understand the transmission dynamics of zoonotic and infectious diseases. In simple terms, accurately anticipating a disease outbreak will enable the local health authorities to evaluate the impact of prevention and control programmes in reducing morbidity/mortality, and by so doing, averting the need of costly emergency responses and lessening the impact on national and household economies and on human health. Disease outbreak models and trends will be used to make informed decisions on the relative risk and benefits of preventive measures aimed at managing the risk at low levels prior to infection and create a more resilient society.

The risk network models (parameterized from and validated against the field data) will be used to test different ways of allocating resources to surveillance, by species and location, in order to maximize cost-effectiveness (as DALY-weighted numbers of cases detected). The reduced risk of zoonotic diseases in livestock achieved through enhanced surveillance will improve the marketability of livestock products, nationally and internationally, across the entire study area. This will especially benefit those intending to farm commercially, at however small a scale, and all those involved in the value chain for livestock and livestock products. This will add in the operational capacity and evidence of cost-effectiveness.

A value chain approach (integrated with risk analysis) will also be utilized in the surveillance of zoonotic and infectious diseases because it describes farming systems in a context and therefore describes weak links and opportunities that can amplify disease risk. Value chains also have a number of influences such as consumer demand, agro-ecological factors, farming system, access to technology, access to resources, livelihoods, poverty, institutions, governance etc) any of which can become an important driver of disease emergence and spread.  There are obvious limitations to studying single diseases or drivers in isolation (e.g. changes that favour one disease may reduce the risk of another; or effects due to changes in one driver may be outweighed by changes in another). The high quality data to be collected, supported by state-of-the-art, diagnostics, genetics, and economic, statistical and mathematical modeling, will address such limitations.

The goal of this work package is therefore to:

• Identify and quantify risk factors for presence of a zoonotic infection in specific individuals, markets, slabs, hospitals or clinics;
• Identify the risk of infection by distinct serotypes in a case-case format;
• Assess the rapid detection of disease outbreaks and
• Map the rural to urban livestock product value chains