One Health approach to measure the impact on wellbeing of selected infectious diseases in humans and animals in Zambia

This study describes the results of a cross-sectional survey conducted in Mambwe district in the Eastern Province in Zambia. It uses a One Health approach to assess the impact of veterinary, medical, environmental and social determinants on animal and human health and wellbeing. One Health is defined as a holistic and interdisciplinary approach that describes the complexities between people, animals, the environment and their health. Human wellbeing is defined in this thesis as ‘a condition in which all members of society are able to determine and meet their needs and have a large range of choices to meet their potential’ (Prescott-Allen, 2001). As a first step, eight focus group discussions with the inhabitants followed by key informant interviews with stakeholders in the area were conducted to give a primary impression and narrow down the problems in relation to animal and human health of the area in general. Following this, a randomized selection of 210 households was visited and in each household blood samples were taken from all humans and all animals belonging to five animal species, namely cattle, goats, sheep, pigs and dogs. A third of the households did not keep any of the animal species chosen for sampling, but their inclusion was important for the social analysis. In all of these 210 households a wellbeing questionnaire was administered and, for every human and animal sampled, a health questionnaire. The study area falls within the tsetse-infested region of Zambia. It has a high wildlife density reflecting the proximity of several national parks and is historically endemic for both human and animal African trypanosomiasis (HAT&AAT). Therefore humans and animals were tested for trypanosomiasis using internal transcribed spacer (ITS) polymerase chain reaction (PCR). Since it is important as a differential diagnosis, malaria was tested for by a rapid diagnostic test in the field from human blood. Sera from mature individuals from all animal species except pigs were tested in a field laboratory for brucellosis using the Rose Bengal test. Additionally, cattle and dogs were tested for five genera of tick-borne infections (TBI) including Anaplasma, Ehrlichia, Theileria, Babesia and Rickettsia using reverse line blot (RLB) in the laboratory at the University of Edinburgh (UoE). The blood samples for PCR and RLB analysis at UoE were stored on WhatmanTM FTA cards. A total of 1012 human samples were tested for HAT and none found positive. 1005 (seven people had been tested positive or treated against malaria shortly before the sampling) people tested for malaria showed an overall prevalence of 15% (95% CI 13.2-17.7). None of the 734 Rose Bengal tests showed up positive for brucellosis. The prevalence of AAT in 1275 samples tested was much lower compared to former samplings; in cattle 22% (95% CI 18-27.2), in goats 7% (95% CI 4.5-9.2), in pigs 6% (95% CI 3.2-9.4), in dogs 9% (95% CI 5.2-13.6) and no samples were found positive in sheep. The prevalence of TBIs is much more complex with many multiple infections. A total of 340 cattle and 195 dogs were tested. In cattle the number of samples positive for any microorganism was as follows; 92% (95% CI 88- 94.2). Overall there were fewer positive samples from dogs with 25% of animals infected (95% CI 19.2-31.8). The wellbeing and health questionnaires were designed to help to identify possible risk factors for the above-mentioned diseases and signs, such as fever, diarrhoea and seizures, indicative for several other diseases. The results of these surveys might also help to identify potential reasons for a lower or higher prevalence of trypanosomiasis and malaria found than expected from previous studies. Additionally, information on personal happiness, attitudes towards veterinary and medical services, medical treatments received, education, women’s reproductive history, drug abuse, people’s perceptions of changes in environment and agriculture, demography, poverty and migration were collected via the questionnaires alongside information on livestock demographics and fertility. One of the main conclusions is that both medical and veterinary health care systems suffer from a number of shortcomings. The distance to appropriate treatment and care facilities is far and the necessary drugs are often unavailable. Also, both the knowledge and technology for diagnosing selected diseases is not in place. This study suggests that neurocysticercosis (NCC) plays an important role in this area due to the high number of seizures reported in people, in whom treatment for epilepsy was unsuccessful. Samples taken from a few pigs indicated the presence of Taenia solium, the causal agent of NCC. Furthermore, many of the TBIs are of zoonotic nature and further investigations must be made to begin to assess the burden of these diseases in humans and animals. Environmental changes such as degradation of the vegetation are likely to have an influence on the prevalence of studied diseases and this aspect is being investigated further in other studies. Due to the nature of a cross-sectional study, only limited conclusions can be drawn on the causal relationships of disease prevalence, but the social analysis conducted in this study confirmed the interactions of selected factors related to health and wealth unique for this study area

A Multi-Host Agent-Based Model for a Zoonotic, Vector-Borne Disease. A Case Study on Trypanosomiasis in Eastern Province, Zambia

Background: This paper presents a new agent-based model (ABM) for investigating T. b. rhodesiense human African trypanosomiasis (rHAT) disease dynamics, produced to aid a greater understanding of disease transmission, and essential for development of appropriate mitigation strategies. Methods: The ABM was developed to model rHAT incidence at a fine spatial scale along a 75 km transect in the Luangwa Valley, Zambia. The method offers a complementary approach to traditional compartmentalised modelling techniques, permitting incorporation of fine scale demographic data such as ethnicity, age and gender into the simulation. Results: Through identification of possible spatial, demographic and behavioural characteristics which may have differing implications for rHAT risk in the region, the ABM produced output that could not be readily generated by other techniques. On average there were 1.99 (S.E. 0.245) human infections and 1.83 (S.E. 0.183) cattle infections per 6 month period. The model output identified that the approximate incidence rate (per 1000 person-years) was lower amongst cattle owning households (0.079, S.E. 0.017), than those without cattle (0.134, S.E. 0.017). Immigrant tribes (e.g. Bemba I.R. = 0.353, S.E.0.155) and school-age children (e.g. 5–10 year old I.R. = 0.239, S.E. 0.041) were the most at-risk for acquiring infection. These findings have the potential to aid the targeting of future mitigation strategies. Conclusion: ABMs provide an alternative way of thinking about HAT and NTDs more generally, offering a solution to the investigation of local-scale questions, and which generate results that can be easily disseminated to those affected. The ABM can be used as a tool for scenario testing at an appropriate spatial scale to allow the design of logistically feasible mitigation strategies suggested by model output. This is of particular importance where resources are limited and management strategies are often pushed to the local scale. © 2016 Alderton et al

Exploiting Human Resource Requirements to Infer Human Movement Patterns for Use in Modelling Disease Transmission Systems:An Example from Eastern Province, Zambia

In this research, an agent-based model (ABM) was developed to generate human movement routes between homes and water resources in a rural setting, given commonly available geospatial datasets on population distribution, land cover and landscape resources. ABMs are an object-oriented computational approach to modelling a system, focusing on the interactions of autonomous agents, and aiming to assess the impact of these agents and their interactions on the system as a whole. An A* pathfinding algorithm was implemented to produce walking routes, given data on the terrain in the area. A* is an extension of Dijkstra’s algorithm with an enhanced time performance through the use of heuristics. In this example, it was possible to impute daily activity movement patterns to the water resource for all villages in a 75 km long study transect across the Luangwa Valley, Zambia, and the simulated human movements were statistically similar to empirical observations on travel times to the water resource (Chi-squared, 95% confidence interval). This indicates that it is possible to produce realistic data regarding human movements without costly measurement as is commonly achieved, for example, through GPS, or retrospective or real-time diaries. The approach is transferable between different geographical locations, and the product can be useful in providing an insight into human movement patterns, and therefore has use in many human exposure-related applications, specifically epidemiological research in rural areas, where spatial heterogeneity in the disease landscape, and space-time proximity of individuals, can play a crucial role in disease spread

Sample village sites (red circles) and observed boreholes (blue circles) in the study area, Luangwa Valley, Zambia (Produced using Landsat 7 imagery from USGS).

<p>Sample village sites (red circles) and observed boreholes (blue circles) in the study area, Luangwa Valley, Zambia (Produced using Landsat 7 imagery from USGS).</p

Example path produced using the A* and land classification between arbitrary points.

<p>Arbitrary points were used to emphasise how the algorithm diverts the path around a prominent obstacle; in this case, the river itself (Produced using Bing aerial imagery).</p

Matrix of Chi-squared results for varying cost weighting and borehole distance threshold; bold indicates statistically significant (95% conf.).

<p>Matrix of Chi-squared results for varying cost weighting and borehole distance threshold; bold indicates statistically significant (95% conf.).</p

Flow chart of all methods used to produce the agent paths and movement times to calibrate to real world data.

<p>Flow chart of all methods used to produce the agent paths and movement times to calibrate to real world data.</p

Individual errors for the single agent per village simulation using the H10G25 heuristic and 1 km borehole threshold.

<p>The square root of the errors is shown, for ease of interpretation (Produced using Landsat 7 imagery from USGS.)</p

Chi-squared values and standard deviations for ten repeat runs of each borehole threshold for heuristic model H10G25.

<p>The red line shows 95% significance.</p

Distribution of the individual errors (difference between simulation and questionnaire results) for the H10G25 simulation, with a 1 km borehole threshold.

<p>Distribution of the individual errors (difference between simulation and questionnaire results) for the H10G25 simulation, with a 1 km borehole threshold.</p