Spatial Optimization Methods for Malaria Risk Mapping in Sub-Saharan African Cities Using Demographic and Health Surveys

Vector-borne diseases, such as malaria, are affected by the rapid urban growth and climate change in sub-Saharan Africa (SSA). In this context, intra-urban malaria risk maps act as a key decision-making tool for targeting malaria control interventions, especially in resource-limited settings. The Demographic and Health Surveys (DHS) provide a consistent malaria data source for mapping malaria risk at the national scale, but their use is limited at the intra-urban scale because survey cluster coordinates are randomly displaced for ethical reasons. In this research, we focus on predicting intra-urban malaria risk in SSA cities-Dakar, Dar es Salaam, Kampala and Ouagadougou-and investigate the use of spatial optimization methods to overcome the effect of DHS spatial displacement. We modeled malaria risk using a random forest regressor and remotely sensed covariates depicting the urban climate, the land cover and the land use, and we tested several spatial optimization approaches. The use of spatial optimization mitigated the effects of DHS spatial displacement on predictive performance. However, this comes at a higher computational cost, and the percentage of variance explained in our models remained low (around 30%-40%), which suggests that these methods cannot entirely overcome the limited quality of epidemiological data. Building on our results, we highlight potential adaptations to the DHS sampling strategy that would make them more reliable for predicting malaria risk at the intra-urban scale

Point pattern simulation modelling of extensive and intensive chicken farming in Thailand : accounting for clustering and landscape characteristics

In recent decades, intensification of animal production has been occurring rapidly in transition economies to meet the growing demands of increasingly urban populations. This comes with significant environmental, health and social impacts. To assess these impacts, detailed maps of livestock distributions have been developed by downscaling census data at the pixel level (10 km or 1 km), providing estimates of the density of animals in each pixel. However, these data remain at fairly coarse scale and many epidemiological or environmental science applications would make better use of data where the distribution and size of farms are predicted rather than the number of animals per pixel. Based on detailed 2010 census data, we investigated the spatial point pattern distribution of extensive and intensive chicken farms in Thailand. We parameterized point pattern simulation models for extensive and intensive chicken farms and evaluated these models in different parts of Thailand for their capacity to reproduce the correct level of spatial clustering and the most likely locations of the farm clusters. We found that both the level of clustering and location of clusters could be simulated with reasonable accuracy by our farm distribution models. Furthermore, intensive chicken farms tended to be much more clustered than extensive farms, and their locations less easily predicted using simple spatial factors such as human populations. These point-pattern simulation models could be used to downscale coarse administrative level livestock census data into farm locations. This methodology could be of particular value in countries where farm location data are unavailable

The Multi-Satellite Environmental and Socioeconomic Predictors of Vector-Borne Diseases in African Cities:Malaria as an Example

Remote sensing has been used for decades to produce vector-borne disease risk maps aiming at better targeting control interventions. However, the coarse and climatic-driven nature of these maps largely hampered their use in the fight against malaria in highly heterogeneous African cities. Remote sensing now offers a large panel of data with the potential to greatly improve and refine malaria risk maps at the intra-urban scale. This research aims at testing the ability of different geospatial datasets exclusively derived from satellite sensors to predict malaria risk in two sub-Saharan African cities: Kampala (Uganda) and Dar es Salaam (Tanzania). Using random forest models, we predicted intra-urban malaria risk based on environmental and socioeconomic predictors using climatic, land cover and land use variables among others. The combination of these factors derived from different remote sensors showed the highest predictive power, particularly models including climatic, land cover and land use predictors. However, the predictive power remained quite low, which is suspected to be due to urban malaria complexity and malaria data limitations. While huge improvements have been made over the last decades in terms of remote sensing data acquisition and processing, the quantity and quality of epidemiological data are not yet sufficient to take full advantage of these improvements

Intensification of chicken production : farm distribution modelling to inform disease transmission models

Population and income growth, urbanisation and technological advances have led to intensification of livestock production systems over the past decades. This has significant health and environmental impacts (e.g. soil pollution and spread of epidemics). A trend in intensification is observable globally, but the level of intensification varies between countries. As the characteristics and the distribution of farms appear to change as a result of intensification, the impacts of livestock production systems are expected to evolve. This creates a need for a better understanding of the spatial factors influencing intensive farming. We also need high-resolution maps of livestock production systems, which are crucial to help assess the impacts of livestock intensification process. This dissertation explores the spatial features of chicken production and elaborates methods to predict chicken farm locations. First, we characterise the spatial constraints of chicken farmers in their early stages of intensification, using survey data from western Kenya. We found a diversity of semi-intensive farms in this region and show that local conditions affect production management types. Second, we build farm distribution models (FDMs) to predict chicken farms’ location and size in four countries spread along a gradient of intensification; Nigeria, Thailand, Argentina and Belgium. We also test the ability of the FDMs to simulate the spread of highly pathogenic avian influenza H5N1 in Bangladesh. Using point pattern analysis method, we developed FDMs that account for farm clustering. This allows for a more realistic spatial representation of livestock systems. Upon further validation, this method could represent an interesting tool to model the transmission of epidemic diseases in livestock systems.(SC - Sciences) -- UCL, 201

Characterization of small-scale commercial chicken production in a distant rural area of a lower-middle-income country

Poultry production can enhance the livelihoods of rural people. Poultry production in low andmiddle-income countries is dominated by small-scale backyard systems with low inputs and lowoutputs. Poultry production, productivity and generated income can be enhanced through intensi-fication; the provision of inputs such as improved breeds, feed, housing and health-care. In Kenya,poultry production systems encompass free-range, semi-intensive and intensive systems. Despite agrowing intensive sector, mostly located in and around Nairobi and other cities, indigenous chickensstill dominate poultry production. However, their productivity could be improved in semi-intensiveand intensive systems. Intensification is a relatively recent process in low- and middle-income coun-tries compared to high-income countries. The complex reality of smallholders trying to improvetheir production is poorly understood and described. We explored the commercial chicken sector ina rural area distant form major production centres, and developed a fine-scale typology of commer-cial farms in western Kenya. We surveyed 111 chicken commercial farms in 2016. We targeted farmswho sell the majority of their production, with 50 chickens or more, and in which animals wereat least partly confined and were provided feeds. Farms were found mainly to raise dual-purposeindigenous chickens in association with crop production and were not specialised towards any par-ticular product or market. Although the farmers interviewed shared many features of free-rangesystem, they expressed the wish to make a commercial activity of their chicken production, withlarge flocks and management similar to semi-intensive farms. Four types of farms were identifiedbased on two groups of variables, related to intensification and accessibility; (i) remote, small-scaleold farms, with small flocks, using a lot of their own crops as feed, (ii) medium-scale, old farmswith a lar- ger flock and well located (iii) large-scale young farms, with large flocks, (iii-a) welllocated and who buy their chicks and (iii-b) remotely located and who hatch their chicks. Thesegroups sit along a gradient of intensification. Location, which affects access to markets and inputs,determines the opportunities available to farmers and thus gives rise to further diversity in farmmanagement types. We found that commercial chicken farms in western Kenya varied greatly interms of management, opportunities and challenges

Poultry farm distribution models developed along a gradient of intensification

Efficient planning of measures limiting epidemic spread requires information on farm locations and sizes (number of animals per farm). However, such data are rarely available. The intensification process which is operating in most low- and middle-income countries (LMICs), comes together with a spatial clustering of farms, a characteristic epidemiological models are sensitive to. We developed farm distribution models predicting both the location and the number of animals per farm, while accounting for the spatial clustering of farms in data-poor countries, using poultry production as an example. We selected four countries, Nigeria, Thailand, Argentina and Belgium, along a gradient of intensification expressed by the per capita Gross Domestic Product (GDP). First, we investigated the distribution of chicken farms along the spectrum of intensification. Second, we built farm distribution models (FDM) based on censuses of commercial farms of each of the four countries, using point pattern and random forest models. As an external validation, we predicted farm locations and sizes in Bangladesh. The number of chicken per farm increased gradually in line with the gradient of GDP per capita in the following order: Nigeria, Thailand, Argentina and Belgium. Interestingly, we did not find such a gradient for farm clustering. Our modelling procedure could only partly reproduce the observed datasets in each of the four sample countries in internal validation. However, in the external validation, the clustering of farms could not be reproduced and the spatial predictors poorly explained the number and location of farms and farm sizes in Bangladesh. Further improvements of the methodology should explore other covariates of the intensity of farms and farm sizes, as well as improvements of the methodology. Structural transformation, economic development and environmental conditions are essential characteristics to consider for an extrapolation of our FDM procedure, as generalisation appeared challenging. We believe the FDM procedure could ultimately be used as a predictive tool in data-poor countries

Modelling emerging intensive poultry sector at farm-level in western Kenya

Intensification of livestock production occurs in each part of the world, although at different speeds. However, it comes with many environmental, health and societal impacts. Assessing these impacts requires to know the geographical distribution of livestock. Therefore, understanding underlying spatial patterns of livestock distribution and its evolution with intensification is essential to further explore how these distributions may be associated with zoonotic disease risk, for example. The spatial distribution of farms and the factors influencing it, tend to vary between production systems (extensive backyard household vs commercial larger intensive farms). Extensive systems are generally homogeneously distributed and correlated to rural populations, especially in developing and transition economies. Intensive farms tend to cluster and to be more closely associated to accessibility to inputs and markets than to land resources. However, factors influencing the distribution of intensive farming are poorly known, especially at fine scales, and their assessment suffers from a lack of spatially explicit data sets with precise farm locations and characteristics. This is particularly true in developing countries, where farm locations are not recorded formally. Although extensive backyard systems still dominate in Kenya, an intensive poultry sector is emerging. We aimed to characterize the different farm types of this emerging commercial chicken sector and to determine their spatial distribution. For that purpose, we collected data on all commercial farms (50-1000 birds) in western Kenya, in an area of 40km diameter at the intersection of Busia, Bungoma and Kakamega counties. A cluster analysis will be used to characterize the different types of farmers of the early stages of intensification process using both individual characteristics (chicken breeds raised, feed types and destination of output types) and contextual variables (access to market and access to city). We will also test if the numbers of animals well represent intensification level of a farm, a hypothesis assumed in previous works at broader scales. Finally, spatial determinants of farm types in western Kenya will be investigated to identify which characteristics make some areas more suitable for the establishment of the different types of farm. This study will allow the understanding of the factors encouraging farmers to start a commercial activity. This will in turn help further modelling of chicken populations at farm-level, i.e. predicting farm location and size

Downscaling livestock census data using multivariate predictive models: Sensitivity to modifiable areal unit problem

The analysis of census data aggregated by administrative units introduces a statistical bias known as the modifiable areal unit problem (MAUP). Previous researches have mostly assessed the effect of MAUP on upscaling models. The present study contributes to clarify the effects of MAUP on the downscaling methodologies, highlighting how a priori choices of scales and shapes could influence the results. We aggregated chicken and duck fine-resolution census in Thailand, using three administrative census levels in regular and irregular shapes. We then disaggregated the data within the Gridded Livestock of the World analytical framework, sampling predictors in two different ways. A sensitivity analysis on Pearson’s r correlation statistics and RMSE was carried out to understand how size and shapes of the response variables affect the goodness-of-fit and downscaling performances. We showed that scale, rather than shapes and sampling methods, affected downscaling precision, suggesting that training the model using the finest administrative level available is preferable. Moreover, datasets showing non-homogeneous distribution but instead spatial clustering seemed less affected by MAUP, yielding higher Pearson’s r values and lower RMSE compared to a more spatially homogenous dataset. Implementing aggregation sensitivity analysis in spatial studies could help to interpret complex results and disseminate robust products