Spatial and temporal distribution of foot-and-mouth disease in four districts situated along the Uganda-Tanzania border: Implications for cross-border efforts in disease control

A unified Foot and Mouth Disease dataset for Uganda: evaluating machine learning predictive performance degradation under varying distributions

In Uganda, the absence of a unified dataset for constructing machine learning models to predict Foot and Mouth Disease outbreaks hinders preparedness. Although machine learning models exhibit excellent predictive performance for Foot and Mouth Disease outbreaks under stationary conditions, they are susceptible to performance degradation in non-stationary environments. Rainfall and temperature are key factors influencing these outbreaks, and their variability due to climate change can significantly impact predictive performance. This study created a unified Foot and Mouth Disease dataset by integrating disparate sources and pre-processing data using mean imputation, duplicate removal, visualization, and merging techniques. To evaluate performance degradation, seven machine learning models were trained and assessed using metrics including accuracy, area under the receiver operating characteristic curve, recall, precision and F1-score. The dataset showed a significant class imbalance with more non-outbreaks than outbreaks, requiring data augmentation methods. Variability in rainfall and temperature impacted predictive performance, causing notable degradation. Random Forest with borderline SMOTE was the top-performing model in a stationary environment, achieving 92% accuracy, 0.97 area under the receiver operating characteristic curve, 0.94 recall, 0.90 precision, and 0.92 F1-score. However, under varying distributions, all models exhibited significant performance degradation, with random forest accuracy dropping to 46%, area under the receiver operating characteristic curve to 0.58, recall to 0.03, precision to 0.24, and F1-score to 0.06. This study underscores the creation of a unified Foot and Mouth Disease dataset for Uganda and reveals significant performance degradation in seven machine learning models under varying distributions. These findings highlight the need for new methods to address the impact of distribution variability on predictive performance.

Seroprevalence of contagious bovine pleuropneumonia (CBPP) in cattle from Karamoja region, North-eastern Uganda

BACKGROUND

Contagious bovine pleuropneumonia [CBPP] is a transboundary animal disease of cattle caused by Mycoplasma mycoides subsp. mycoides [Mmm]. CBPP causes severe economic losses to livestock producers in sub-Saharan Africa mainly due to high mortality, morbidity, reduction in productivity as well as livestock trade restrictions. This study aimed at determining seroprevalence of Mmm in cattle from Karamoja region, north-eastern Uganda; data that are required to design and implement risk based CBPP control program.

METHODS

We randomly collected blood samples from 2,300 cattle spread across Karamoja region. Serum was extracted and screened for antibodies against Mycoplasma mycoides subsp. mycoides [Mmm] using the competitive enzyme linked immunosorbent assay [cELISA].

RESULTS

A quarter [25.4%; 95% CI: 23.7-27.3] of the screened cattle [n = 2,300] were sero-positive for Mmm. Amudat and Kaabong districts recorded the lowest [12.3%] and highest [30.7%] Mmm seroprevalence respectively. Increasing age, overnight stay in cattle kraals and location [certain districts, villages, herds and sub counties] of the cattle herds, the factors that promote animal commingling, were the most significant risk factors of seroconversion with Mmm.

CONCLUSION

Results from this study indicated a higher seroprevalence of Mmm in Karamoja region cattle herds. This could be due to the increased frequency of CBPP outbreaks in recent years. To be effective, CBPP vaccination programs should target high risk herds along the international borders and other hotspot areas [e.g., parishes or sub counties] where cattle commingling is high.

A Bayesian inference method to estimate transmission trees with multiple introductions; applied to SARS-CoV-2 in Dutch mink farms

Knowledge of who infected whom during an outbreak of an infectious disease is important to determine risk factors for transmission and to design effective control measures. Both whole-genome sequencing of pathogens and epidemiological data provide useful information about the transmission events and underlying processes. Existing models to infer transmission trees usually assume that the pathogen is introduced only once from outside into the population of interest. However, this is not always true. For instance, SARS-CoV-2 is suggested to be introduced multiple times in mink farms in the Netherlands from the SARS-CoV-2 pandemic among humans. Here, we developed a Bayesian inference method combining whole-genome sequencing data and epidemiological data, allowing for multiple introductions of the pathogen in the population. Our method does not a priori split the outbreak into multiple phylogenetic clusters, nor does it break the dependency between the processes of mutation, within-host dynamics, transmission, and observation. We implemented our method as an additional feature in the R-package phybreak. On simulated data, our method correctly identifies the number of introductions, with an accuracy depending on the proportion of all observed cases that are introductions. Moreover, when a single introduction was simulated, our method produced similar estimates of parameters and transmission trees as the existing package. When applied to data from a SARS-CoV-2 outbreak in Dutch mink farms, the method provides strong evidence for independent introductions of the pathogen at 13 farms, infecting a total of 63 farms. Using the new feature of the phybreak package, transmission routes of a more complex class of infectious disease outbreaks can be inferred which will aid infection control in future outbreaks.

Identifying target areas for risk-based surveillance and control of transboundary animal diseases: a seasonal analysis of slaughter and live-trade cattle movements in Uganda

Animal movements are a major driver for the spread of Transboundary Animal Diseases (TADs). These movements link populations that would otherwise be isolated and hence create opportunities for susceptible and infected individuals to meet. We used social network analysis to describe the seasonal network structure of cattle movements in Uganda and unravel critical network features that identify districts or sub-regions for targeted risk-based surveillance and intervention. We constructed weighted, directed networks based on 2019 between-district cattle movements using official livestock mobility data; the purpose of the movement ('slaughter' vs. 'live trade') was used to subset the network and capture the risks more reliably. Our results show that cattle trade can result in local and long-distance disease spread in Uganda. Seasonal variability appears to impact the structure of the network, with high heterogeneity of node and edge activity identified throughout the seasons. These observations mean that the structure of the live trade network can be exploited to target influential district hubs within the cattle corridor and peripheral areas in the south and west, which would result in rapid network fragmentation, reducing the contact structure-related trade risks. Similar exploitable features were observed for the slaughter network, where cattle traffic serves mainly slaughter hubs close to urban centres along the cattle corridor. Critically, analyses that target the complex livestock supply value chain offer a unique framework for understanding and quantifying risks for TADs such as Foot-and-Mouth disease in a land-locked country like Uganda. These findings can be used to inform the development of risk-based surveillance strategies and decision making on resource allocation. For instance, vaccine deployment, biosecurity enforcement and capacity building for stakeholders at the local community and across animal health services with the potential to limit the socio-economic impact of outbreaks, or indeed reduce their frequency.

Factors associated with foot-and-mouth disease seroprevalence in small ruminants and identification of hot-spot areas in northern Nigeria

Many small ruminants infected with foot-and-mouth disease (FMD) remain asymptomatic, with the capacity to promote silent viral spread within domestic and wildlife species. However, little is known about the epidemiological role played by small ruminants in FMD. In particular, there are few studies that examine FMD seroprevalence, spatial patterns and risk factors for exposure in small ruminants. A cross-sectional study was conducted in northern Nigeria (Bauchi, Kaduna, and Plateau States) to determine the true seroprevalence of FMD in backyard small ruminants, identify factors associated with FMD seroconversion at animal and household levels, and identify spatial patterns for FMD virus exposure. Data on animal (n = 1800) and household (n = 300) characteristics were collected using a standardised questionnaire. Sera samples from 1800 small ruminants were tested for antibodies against non-structural proteins of FMD virus. True seroprevalence was estimated stochastically to account for variability and uncertainty in the test sensitivity and specificity previously reported. Risk factors for FMD seropositivity were identified at animal and household levels and spatial patterns were determined. The overall true seroprevalence for FMD virus, in the small ruminant population tested, was estimated to be 10.2 % (95 % Credible Interval (CrI) 0.0-19.0), while State-level estimates were 17.3 % (95 % CrI 0.0-25.8) for Kaduna, 6.9 % (95% CrI 0.0-15.8) for Bauchi, and 3.6 % (95 % CrI 0.0-12.6) for Plateau. State and species were the main risk factors identified at animal level, with interaction detected between them. Compared to goats in Plateau, the odds of testing positive were higher for goats in Bauchi (Odds Ratio (OR)= 1.83, 95 % CI 1.13-2.97, p = 0.01) and Kaduna (OR=2.97, 95 % CI 1.89-4.67, p < 0.001), as well as for sheep in Plateau (OR=3.78, 95 % CI 2.08-6.87, p < 0.001), Bauchi (OR=1.61, 95 % CI 0.91-2.84, p = 0.10), and Kaduna (OR=3.11, 95 % CI 1.61-6.01, p = 0.001). Households located in Kaduna were more likely to have a higher number of seropositive SR compared to those in Plateau (Prevalence Ratio (PR)= 1.75, 95 % CI 1.30-2.36, p < 0.001), and households keeping sheep flocks were more likely to be seropositive (from 1 to 10 sheep: PR=1.39, 95 % CI 1.05-1.82, p = 0.02; more than 10 sheep: PR=1.55, 95 % CI 1.12-2.15, p = 0.008) compared to those that did not keep sheep. A hot-spot was detected in Kaduna, and a cold-spot in Plateau. These results reveal that small ruminants had been recently exposed to FMD virus with spatial heterogeneity across the study area.

A scoping review of foot-and-mouth disease risk, based on spatial and spatio-temporal analysis of outbreaks in endemic settings

Foot-and-mouth disease (FMD) is one of the most important transboundary animal diseases affecting livestock and wildlife species worldwide. Sustained viral circulation, as evidenced by serological surveys and the recurrence of outbreaks, suggests endemic transmission cycles in some parts of Africa, Asia and the Middle East. This is the result of a complex process in which multiple serotypes, multi-host interactions and numerous socio-epidemiological factors converge to facilitate disease introduction, survival and spread. Spatial and spatio-temporal analyses have been increasingly used to explore the burden of the disease by identifying high-risk areas, analysing temporal trends and exploring the factors that contribute to the outbreaks. We systematically retrieved spatial and spatial-temporal studies on FMD outbreaks to summarize variations on their methodological approaches and identify the epidemiological factors associated with the outbreaks in endemic contexts. Fifty-one studies were included in the final review. A high proportion of papers described and visualized the outbreaks (72.5%) and 49.0% used one or more approaches to study their spatial, temporal and spatio-temporal aggregation. The epidemiological aspects commonly linked to FMD risk are broadly categorizable into themes such as (a) animal demographics and interactions, (b) spatial accessibility, (c) trade, (d) socio-economic and (e) environmental factors. The consistency of these themes across studies underlines the different pathways in which the virus is sustained in endemic areas, with the potential to exploit them to design tailored evidence based-control programmes for the local needs. There was limited data linking the socio-economics of communities and modelled FMD outbreaks, leaving a gap in the current knowledge. A thorough analysis of FMD outbreaks requires a systemic view as multiple epidemiological factors contribute to viral circulation and may improve the accuracy of disease mapping. Future studies should explore the links between socio-economic and epidemiological factors as a foundation for translating the identified opportunities into interventions to improve the outcomes of FMD surveillance and control initiatives in endemic contexts.

Investigation of foot and mouth disease virus and other animal pathogens in cattle, buffaloes and goats at the interface with Akagera National Park 2017 - 2020

BACKGROUND

Foot-and-Mouth Disease Virus (FMDV) is a positive-sense RNA virus of the family of the picornaviridæ that is responsible for one of the livestock diseases with the highest economic impact, the Foot-and-Mouth Disease (FMD). FMD is endemic in Rwanda but there are gaps in knowing its seroprevalence and molecular epidemiology. This study reports the FMD seroprevalence and molecular characterization of FMDV in Eastern Rwanda.

RESULTS

The overall seroprevalence of FMD in the study area is at 9.36% in cattle and 2.65% in goats. We detected FMDV using molecular diagnostic tools such as RT-PCR and RT-LAMP and the phylogenetic analysis of the obtained sequences revealed the presence of FMDV serotype SAT 2, lineage II. Sequencing of the oropharyngeal fluid samples collected from African buffaloes revealed the presence of Prevotela ruminicola, Spathidium amphoriforme, Moraxella bovoculi Onchocerca flexuosa, Eudiplodinium moggii, Metadinium medium and Verrucomicrobia bacterium among other pathogens but no FMDV was detected in African buffaloes.

CONCLUSIONS

We recommend further studies to focus on sampling more African buffaloes since the number sampled was statistically insignificant to conclusively exclude the presence or absence of FMDV in Eastern Rwanda buffaloes. The use of RT-PCR alongside RT-LAMP demonstrates that the latter can be adopted in endemic areas such as Rwanda to fill in the gaps in terms of molecular diagnostics. The identification of lineage II of SAT 2 in Rwanda for the first time shows that the categorised FMDV pools as previously established are not static over time.

Spatio-temporal cluster analysis and transmission drivers for Peste des Petits Ruminants in Uganda

Peste des Petits Ruminants (PPR) is a transboundary, highly contagious, and fatal disease of small ruminants. PPR causes global annual economic losses of between USD 1.5-2.0 billion across more than 70 affected countries. Despite the commercial availability of effective PPR vaccines, lack of financial and technical commitment to PPR control coupled with a dearth of refined PPR risk profiling data in different endemic countries has perpetuated PPR virus transmission. In Uganda, over the past five years, PPR has extended from north-eastern Uganda (Karamoja) with sporadic incursions in other districts /regions. To identify disease cluster hotspot trends that would facilitate the design and implementation of PPR risk-based control methods (including vaccination), we employed the space-time cube approach to identify trends in the clustering of outbreaks in neighbouring space-time cells using confirmed PPR outbreak report data (2007-2020). We also used negative binomial and logistic regression models and identified high small ruminant density, extended road length, low annual precipitation and high soil water index as the most important drivers of PPR in Uganda. The study identified (with 90 - 99% confidence) five PPR disease hotspot trend categories across subregions of Uganda. Diminishing hotspots were identified in the Karamoja region whereas consecutive, sporadic, new, and emerging hotspots were identified in central and southwestern districts of Uganda. Inter-district and cross-border small ruminant movement facilitated by longer road stretches and animal comingling precipitate PPR outbreaks as well as PPR virus spread from its initial Karamoja focus to the central and south-western Uganda. There is therefore urgent need to prioritize considerable vaccination coverage to obtain the required herd immunity among small ruminants in the new hotspot areas to block transmission to further emerging hotspots. Findings of this study provide a basis for more robust timing and prioritization of control measures including vaccination. This article is protected by copyright. All rights reserved.

Risk factors for the incursion, spread and persistence of the foot and mouth disease virus in Eastern Rwanda

BACKGROUND

Identification of risk factors is crucial in Foot-and-mouth disease (FMD) control especially in endemic countries. In Rwanda, almost all outbreaks of Foot-and-Mouth Disease Virus (FMDV) have started in Eastern Rwanda. Identifying the risk factors in this area will support government control efforts. This study was carried out to identify and map different risk factors for the incursion, spread and persistence of FMDV in Eastern Rwanda. Questionnaires were administered during farm visits to establish risk factors for FMD outbreaks. Descriptive statistical measures were determined and odds ratios were calculated to determine the effects of risk factors on the occurrence of FMD. Quantum Geographic Information System (QGIS) was used to produce thematic maps on the proportion of putative risk factors for FMD per village.

RESULTS

Based on farmers' perceptions, 85.31% (with p < 0.01) experienced more outbreaks during the major dry season, a finding consistent with other reports in other parts of the world. Univariate analysis revealed that mixed farming (OR = 1.501, p = 0.163, CI = 95%), and natural breeding method (OR = 1.626; p = 0.21, CI = 95%) were associated with the occurrence of FMD indicating that the two risk factors could be responsible for FMD outbreaks in the farms. The occurrence of FMD in the farms was found to be significantly associated with lack of vaccination of calves younger than 12 months in herds (OR = 0.707; p = 0.046, CI = 95%).

CONCLUSIONS

This is the first study to describe risk factors for persistence of FMDV in livestock systems in Rwanda. However, further studies are required to understand the role of transboundary animal movements and genotypic profiles of circulating FMDV in farming systems in Rwanda.