The effect of livestock density on Trypanosoma brucei gambiense and T. b. rhodesiense : a causal inference-based approach

Domestic and wild animals are important reservoirs of the rhodesiense form of human African trypanosomiasis (rHAT), however quantification of this effect offers utility for deploying non-medical control activities, and anticipating their success when wildlife are excluded. Further, the uncertain role of animal reservoirs—particularly pigs—threatens elimination of transmission (EOT) targets set for the gambiense form (gHAT). Using a new time series of high-resolution cattle and pig density maps, HAT surveillance data collated by the WHO Atlas of HAT, and methods drawn from causal inference and spatial epidemiology, we conducted a retrospective ecological cohort study in Uganda, Malawi, Democratic Republic of Congo (DRC) and South Sudan to estimate the effect of cattle and pig density on HAT risk.

Livestock, pathogens, vectors, and their environment: a causal inference-based approach to estimating the pathway-specific effect of livestock on human African trypanosomiasis risk

Livestock are important reservoirs for many diseases, and investigation of such zoonoses has long been the focus of One Health research. However, the effects of livestock on human and environmental health extend well beyond direct disease transmission. In this retrospective ecological cohort study we use pre-existing data and methods derived from causal inference and spatial epidemiology to estimate three hypothesized mechanisms by which livestock can come to bear on human African trypanosomiasis (HAT) risk: the reservoir effect, by which infected cattle and pigs are a source of infection to humans; the zooprophylactic effect, by which preference for livestock hosts exhibited by the tsetse fly vector of HAT means that their presence protects humans from infection; and the environmental change effect, by which livestock keeping activities modify the environment in such a way that habitat suitability for tsetse flies, and in turn human infection risk, is reduced. We conducted this study in four high burden countries: at the point level in Uganda, Malawi, and Democratic Republic of Congo (DRC), and at the county-level in South Sudan. Our results indicate cattle and pigs play an important reservoir role for the rhodesiense form (rHAT) in Uganda, however zooprophylaxis outweighs this effect for rHAT in Malawi. For the gambiense form (gHAT) we found evidence that pigs may be a competent reservoir, however dominance of the reservoir versus zooprophylactic pathway for cattle varied across countries. We did not find compelling evidence of an environmental change effect.

Preliminary Outbreak Investigation and Spatial Epidemiology of Rabbit Hemorrhagic Disease Outbreak in Nigeria

The outbreak of highly contagious transboundary rabbit hemorrhagic disease (RHD) in Nigeria has a severe socio-economic impact on the rabbit industry. We present the outbreak investigation and spatial epidemiology of the first confirmed RHD outbreak in Nigeria from a field survey of 28 stochastic outbreaks in Kwara State, north-central Nigeria. A total of 1,639 rabbits died from 2,053 susceptible rabbits. The serotype “RHDV-2” was detected in tissue samples from some of the outbreaks. The case fatality rate of the RHDV-2 outbreak was 79.8%. The source of the outbreak is still unknown. Most (71.4%) of the farmers had introduced new rabbits into their farms 1–2 weeks before the outbreak. Most of the farmers practiced biosecurity measures such as farm fencing (83.1%) and routine disinfection of the farm materials (53.6%). However, only 17.8% of the farmers enforced movement restrictions into their farms. Some of the farmers (42.8%) had restocked their farms after being affected by the RHD outbreak and 75% of all those farmers that have restocked had used the RHD vaccine. There was no statistically significant association between adherence to biosecurity measures and the RHD outbreak in affected farms (p = 0.408). However, the introduction of new rabbits into rabbit farms significantly pre-disposed farms to the RHD outbreak (p < 0.001). There is a need for active surveillance of RHD across the country to ensure efficient and effective tracking, monitoring, and control of the disease. Equally, understanding the genetic diversity of the Lagoviruses in Nigeria that cause RHD to aid vaccine development is of utmost importance to prevent future RHD outbreaks.

Spatial epidemiology and genetic diversity of SARS-CoV-2 and related coronaviruses in domestic and wild animals

The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) showed susceptibility to diverse animal species. We conducted this study to understand the spatial epidemiology, genetic diversity, and statistically significant genetic similarity along with per-gene recombination events of SARS-CoV-2 and related viruses (SC2r-CoVs) in animals globally. We collected a number of different animal species infected with SARS-CoV-2 and its related viruses. Then, we retrieved genome sequences of SARS-CoV-2 and SC2r-CoVs from GISAID and NCBI GenBank for genomic and mutational analysis. Although the evolutionary origin of SARS-CoV-2 remains elusive, the diverse SC2r-CoV have been detected in multiple Rhinolophus bat species and in Malayan pangolin. To date, human-to-animal spillover events have been reported in cat, dog, tiger, lion, gorilla, leopard, ferret, puma, cougar, otter, and mink in 25 countries. Phylogeny and genetic recombination events of SC2r-CoVs showed higher similarity to the bat coronavirus RaTG13 and BANAL-103 for most of the genes and to some Malayan pangolin coronavirus (CoV) strains for the N protein from bats and pangolin showed close resemblance to SARS-CoV-2. The clustering of animal and human strains from the same geographical area has proved human-to-animal transmission of the virus. The Alpha, Delta and Mu-variant of SARS-CoV-2 was detected in dog, gorilla, lion, tiger, otter, and cat in the USA, India, Czech Republic, Belgium, and France with momentous genetic similarity with human SARS-CoV-2 sequences. The mink variant mutation (spike_Y453F) was detected in both humans and domestic cats. Moreover, the dog was affected mostly by clade O (66.7%), whereas cat and American mink were affected by clade GR (31.6 and 49.7%, respectively). The α-variant was detected as 2.6% in cat, 4.8% in dog, 14.3% in tiger, 66.7% in gorilla, and 77.3% in lion. The highest mutations observed in mink where the substitution of D614G in spike (95.2%) and P323L in NSP12 (95.2%) protein. In dog, cat, gorilla, lion, and tiger, Y505H and Y453F were the common mutations followed by Y145del, Y144del, and V70I in S protein. We recommend vaccine provision for pet and zoo animals to reduce the chance of transmission in animals. Besides, continuous epidemiological and genomic surveillance of coronaviruses in animal host is crucial to find out the immediate ancestor of SARS-CoV-2 and to prevent future CoVs threats to humans.

Optimal Lockdown in a Commuting Network

We study optimal dynamic lockdowns against COVID-19 within a commuting network. Our framework integrates canonical spatial epidemiology and trade models and is applied to cities with varying initial viral spread: Seoul, Daegu, and the New York City metropolitan area (NYM). Spatial lockdowns achieve substantially smaller income losses than uniform lockdowns. In the NYM and Daegu—with large initial shocks—the optimal lockdown restricts inflows to central districts before gradual relaxation, while in Seoul it imposes low temporal but large spatial variation. Actual commuting reductions were too weak in central locations in Daegu and the NYM and too strong across Seoul. (JEL H51, I12, I18, R23, R41)

A Spatial Analysis of Tuberculosis Related Mortality in South Africa

Background: South Africa, with an estimated annual tuberculosis (TB) incidence of 360,000 cases in 2019, remains one of the countries with the largest burden of TB in the world. The identification of highly burdened TB areas could support public health policy planners to optimally target resources and TB control and prevention interventions. Objective: To investigate the spatial epidemiology and distribution of TB mortality in South Africa in 2010 and its association with area-level poverty and HIV burden. Methods: The study analysed a total of 776,176 TB deaths for the period 2005–2015. Local and global and spatial clustering of TB death rates were investigated by Global and Local Moran’s Indices methods (Moran’s I). The spatial regression analysis was employed to assess the effect of poverty and HIV on TB mortality rates. Results: There was a significant decrease in TB mortality rate, from 179 per 100,000 population in 2005 to 60 per 100,000 population in 2015. The annual TB mortality rate was higher among males (161.5 per 100,000 male population; (95% confidence interval (CI) 132.9, 190.0) than among females (123.2 per 100,000 female population; (95% CI 95.6, 150.8)). The 35–44 age group experienced higher TB mortality rates, regardless of gender and time. Hot spot clusters of TB mortality were found in the South-Eastern parts of the country, whereas cold spot clusters were largely in the north-eastern parts. Tuberculosis death rates were positively associated with poverty, as measured by the South African Multidimension Poverty Index (SAMPI) as well TB death rates in the neighbouring districts. Conclusion: The findings of this study revealed a statistically significant decrease in TB deaths and a disproportionate distribution of TB deaths among certain areas and population groups in South Africa. The existence of the identified inequalities in the burden of TB deaths calls for targeted public health interventions, policies, and resources to be directed towards the most vulnerable populations in South Africa.

Using MapMCDA Tool for the Spatial Epidemiology of Animal Rabies in Morocco: How to Improve the Rationality of a Qualitative Risk Assessment

Objective The objective behind this article is to better characterize spatial distribution of animal rabies in Morocco through qualitative risk assessment framework. In Morocco, the occurrence of the disease is neither clearly distributed nor complete. Therefore, risk assessment methods become strongly recommended to cope with distorted geographic patterns. Methods Based on data collection set from 168 counties, qualitative changes on spatial epidemiology of rabies were analysed by mapMCDA tool covering a period from 2004 to 2017 and including information on determinants of the geographic distribution of animal rabies in Morocco defined in previous work. Results To validate the risk assessment model, the results were compared to rabies cases reported during the study period. The clustering of the rabies risk estimates is decisive and highly reliable. A significant alignment was shown between the very high and high-risk estimates. Conclusion This study is the first attempt that has been made for using MapMCDA for rabies. For a normative process aiming to avoid subjectivity related to expert-opinions, authors suggest conducting initially a statistical multiple component analysis that will provide quantified estimates of risk factors. It would be an advisable decision-making tool that helps to design oriented surveillance and allows better referral of actions to control the disease.

Defining an epidemiological landscape by connecting host movement to pathogen transmission

Environment drives the host movements that shape pathogen transmission through three mediating processes: host density, host mobility, and contact. These processes combine with pathogen life-history to give rise to an “epidemiological landscape” that determines spatial patterns of pathogen transmission. Yet despite its central role in transmission, strategies for predicting the epidemiological landscape from real-world data remain limited. Here, we develop the epidemiological landscape as an interface between movement ecology and spatial epidemiology. We propose a movement-pathogen pace-of-life heuristic for prioritizing the landscape’s central processes by positing that spatial dynamics for fast pace-of-life pathogens are best-approximated by the spatial ecology of host contacts; spatial dynamics for slower pace-of-life pathogens are best approximated by host densities; and spatial dynamics for pathogens with environmental reservoirs reflect a convolution of those densities with the spatial configuration of environmental reservoir sites. We then identify mechanisms that underpin the epidemiological landscape and match each mechanism to emerging tools from movement ecology. Finally, we outline workflows for describing the epidemiological landscape and using it to predict subsequent patterns of pathogen transmission. Our framework links transmission to environmental context, providing a scaffold for mechanistically understanding how environmental context can generate and shift existing patterns in spatial epidemiology.

Geospatial Analysis of Covid-19 to Respond to Pandemic Outbreaks: A Case Study in Bangkok Metropolitan Region, Thailand

Coronavirus (Covid-19) has to date (March 29th 2020) infected over 81,000 Chinese citizens, mostly in Hubei Province, since it was first identified in December 2019. It has so far spread to more than 202 countries. On the 31th of March 2020, the total number of laboratory-confirmed COVID-19 cases reported by Ministry of Public Health (MoPH) in Thailand is 1524, of which 127 have recovered, 1388 are receiving treatment (17 cases are severe) in healthcare settings and nine have died. Recently Geographic Information System (GIS) provide epidemiologists and public health officers in the surveillance, monitoring and controlling of many infected diseases such as vector-borne diseases or human-to-human transmission diseases in many countries. Particularly it can provide the functions of collecting, updating and managing disease surveillance and related data, such as geographical factor and socio-economic. They are also pertinent to suit the needs of understanding the spatial spread or diffusion of disease outbreak and response for designing the prevention and control strategies. The major objective of this research is to apply the spatial epidemiology approaches for studying COVID-19 patterns and hotspots in Bangkok Metropolitan Region (BMR), Thailand. The specific objectives are to analyze the COVID-19 patterns in the terms of population and geographic distribution patterns; to detect the COVID-19 incidence rate under different months by using the spatial analysis. This research provides maps to view the pandemic situation of BMR and the provincial level of COVID-19 in particular heat maps and ring maps.