Outbreak Data Research Articles

Epidemiology of peste des petits ruminants in selected districts of Borena zone, Ethiopia

BackgroundPeste des petits ruminant (PPR) is a contagious disease caused by the peste des petits ruminants virus (PPRV). The disease poses a significant economic threat to small ruminant production in Ethiopia, particularly to the striving pastoral production system. A cross-sectional study was conducted to estimate the seroprevalence and associated risk factors of PPR in the small ruminants of the Borena Zone. A total of 384 serum samples were collected randomly from sheep and goats and examined for the presence of PPRV antibodies using competition enzyme-linked immune sorbent assay (c-ELISA). Additionally, a retrospective analysis of five years of outbreak data was performed to provide insight into the spatial and temporal distribution of the disease.ResultsThe seroprevalence of PPRV antibodies in nonvaccinated, vaccinated, and unknown vaccination status of small ruminants in this study was found to be 32.1%, 68.8%, and 45.5%, respectively. Multivariable logistic analysis revealed a statistically significant association between PPRV seropositivity and several factors, including age, animal origin, flock size, and veterinary services status. A retrospective analysis revealed 53 PPR outbreaks in the Borena Zone from 2018 to 2022, exacerbated by low vaccination coverage relative to the at-risk animal population.ConclusionThe study revealed significant gaps in current vaccination efforts, with herd immunity levels falling below the FAO-WOAH recommended threshold of 80%. Despite Ethiopia’s ambitious goal to eradicate PPR by 2027, the frequent outbreaks and insufficient herd immunity highlight the inadequacy of the existing strategies. To effectively move toward eradication, Ethiopia must align its approach with the global PPR eradication framework, which emphasizes a comprehensive strategy that includes diagnostics, surveillance, prevention, and the establishment of a robust veterinary regulatory system, rather than relying solely on vaccination. Overcoming logistical challenges, improving vaccination coverage, and optimizing the timing of vaccination campaigns, especially in hard-to-reach areas, will be crucial for reducing outbreaks and making progress toward eradication.

Burden of childhood diarrhea and cholera outbreaks in the Lake Tana Basin (Ethiopia): Review

Objectives: Diarrhoea is one of the leading causes of childhood morbidity and mortality, while cholera outbreaks are a major public health emergency insub-Saharan African countries. This study was aimed at investigating the link between climate changes and cholera outbreaks, and the burden of childhood diarrhoea in the Lake Tana Basin (Ethiopia). Methods: Research articles published in English were searched from Google scholar, PubMed, and Web of science and these were supplemented by a four-year secondary data of childhood diarrhoea and cholera outbreaks extracted from health management information system (HMIS) in 61 districts. Results: The mean prevalence of diarrhoea per 1000 children was 420 (95% CI:311.7; 528.6). The prevalence of childhood diarrhoea showed spatial and temporal variation hereby 16.4% (10/61) districts exhibited high prevalence (>201/1000 children). The prevalence of diarrhoea was significantly higher in boys compared to girls (p = 0.001), while the incidence of cholera was significantly higher in females compared to males (p < 0.001). Heavy precipitation and El-Niño events were linked to the episodes of cholera outbreaks. Conclusions: Health interventions should consider spatial and temporal variations of diarrhoea. The cholera outbreak preparedness needs to be aware during the events of climate changes in the hotspot areas. Keywords: Cholera; diarrhoea; climate change; Lake Tana Basin; Ethiopia.

Updates to the wild bird abundance and movement models for the early warning system for avian influenza in the EU

Abstract Highly pathogenic avian influenza (HPAI) viruses pose a significant threat to both poultry and wild bird populations. Migratory wild birds play a key role in the intercontinental spread of avian influenza (AI), introducing the virus into poultry populations. In response to frequent AI outbreaks in Europe, the European Food Safety Authority (EFSA), at the request of the European Commission (EC), produces quarterly and annual epidemiological reports to monitor and analyse AI trends. A key component of this surveillance includes the integration of outbreak data from Member States and contributions from non‐governmental ornithological organisations like the European Bird Census Council (EBCC) and the European Union for Bird Ringing (EURING) together in a predictive spatio‐temporal risk assessment model. Previous data integration and modelling efforts led to the development of an early warning system for predicting HPAI outbreaks accessible through a publicly available online user interface: the Bird Flu Radar. This report presents an improvement of the system by expanding the species coverage and refining the existing base models behind the epidemiological model. Specifically, this report details the exploration to incorporate 12 additional wild bird species into the models, and the changes made to the base models predicting the distribution and movements of wild birds. We demonstrate the improvements respecting the existing base models while at the same time enhancing the effectiveness in predicting HPAI outbreaks and possibly mitigating negative effects in Europe by providing more accurate predictions to different stakeholders.

The responsibility of publishing early outbreak data

The responsibility of publishing early outbreak data

Vaccination governance in protracted conflict settings: the case of northwest Syria

BackgroundEffective vaccination governance in conflict-affected regions poses unique challenges. This study evaluates the governance of vaccination programs in northwest Syria, focusing on effectiveness, efficiency, inclusiveness, data availability, vision, transparency, accountability, and sustainability.MethodsUsing a mixed-methods approach, and adapting Siddiqi’s framework for health governance, data were collected through 14 key informant interviews (KIIs), a validating workshop, and ethnographic observations. Findings were triangulated to provide a comprehensive understanding of vaccination governance.ResultsThe study highlights innovative approaches used to navigate the complex health governance landscape to deliver vaccination interventions, which strengthened sub-national vaccination structures such as The Syria Immunisation Group (SIG). The analysis revealed several key themes. Effectiveness and efficiency were demonstrated through cold-chain reliability and extensive outreach activities, though formal reports lacked detailed analysis of vaccine losses and linkage between disease outbreak data and coverage statistics. Key informants and workshop participants rated the vaccination strategy positively but identified inefficiencies due to irregular funding and bureaucracy. Inclusiveness and data availability were prioritised, with outreach activities targeting vulnerable groups. However, significant gaps in demographic data and reliance on paper-based systems hindered comprehensive coverage analysis. Digitalisation efforts were noted but require further support. The SIG demonstrated a clear strategic vision supported by international organizations such as the World Health Organization, yet limited partner participation in strategic planning raised concerns about broader ownership and engagement. While the SIG was perceived as approachable, the lack of public documentation and financial disclosure limited transparency. Internal information sharing was prevalent, but public communication strategies were insufficient. Accountability and sustainability faced challenges due to a decentralized structure and reliance on diverse donors. Despite stabilizing factors such as decentralization and financial continuity, fragmented oversight and reliance on donor funding remained significant concerns.DiscussionThe study highlights the complexities of vaccination governance in conflict-affected areas. Comparisons with other conflict zones underscore the importance of local organisations and international support. The SIG’s role is pivotal, but its legitimacy, transparency, and inclusivity require improvement. The potential transition to early recovery in Syria poses additional challenges to SIG’s sustainability and integration into national programs.ConclusionThe governance of vaccination in northwest Syria is multifaceted, involving multiple stakeholders and lacking a legitimate government. Enhancing transparency, local ownership, and participatory decision-making are crucial for improving governance. The role of international bodies is essential, emphasising the need for structured feedback mechanisms and transparent monitoring processes to ensure the program’s success and sustainability.

Refining Estimation of the Instantaneous Reproduction Number During Early Pandemic Stages: Addressing Case-Reporting Variability and Serial Interval Uncertainty.

During infectious disease outbreaks, estimates for the instantaneous reproduction number, R(t), are essential for understanding transmission dynamics. This study develops and analyzes new methodology to improve estimation of R(t) when observed case counts are subject to reporting patterns and available serial interval estimates are subject to uncertainty and non-representativeness. Specifically, we developed a Bayesian time-since-infection model with layers to adjust for reporting measurement error, integrate multiple candidate serial interval estimates, and estimate transmission with an autoregressive time-series model incorporating factors relevant to transmission. Additionally, we provide practical tools to identify reporting patterns and determine when to smooth case counts for more usable R(t) estimates. We evaluated model performance relative to widely adopted methodology by simulating outbreak data, finding improved R(t) estimation with the proposed methodology. We also used 2020 COVID-19 data to analyze transmission trends and predictors, identifying strong day-of-week and social distancing effects that subsequently reduced estimate volatility. In addition to new approaches for addressing serial interval uncertainty and incorporating transmission predictor information, this study provides an alternative approach for addressing case-reporting patterns without delaying detection or smoothing over relevant transmission signals. These tools and findings may be used or built upon for current and future outbreaks.

The evolutionary features and roles of single nucleotide variants and charged amino acid mutations in influenza outbreaks during NPI period

The epidemic and outbreaks of influenza B Victoria lineage (Bv) during 2019–2022 led to an analysis of genetic, epitopes, charged amino acids and Bv outbreaks. Based on the National Influenza Surveillance Network (NISN), the Bv 72 strains isolated during 2019–2022 were selected by spatio-temporal sampling, then were sequenced. Using the Compare Means, Correlate and Cluster, the outbreak data were analyzed, including the single nucleotide variant (SNV), amino acid (AA), epitope, evolutionary rate (ER), Shannon entropy value (SV), charged amino acid and outbreak. With the emergence of COVID-19, the non-pharmaceutical interventions (NPIs) made Less distant transmission and only Bv outbreak. The 2021–2022 strains in the HA genes were located in the same subset, but were distinct from the 2019–2020 strains (P < 0.001). The codon G → A transition in nucleotide was in the highest ratio but the transversion of C → A and T → A made the most significant contribution to the outbreaks, while the increase in amino acid mutations characterized by polar, acidic and basic signatures played a key role in the Bv epidemic in 2021–2022. Both ER and SV were positively correlated in HA genes (R = 0.690) and NA genes (R = 0.711), respectively, however, the number of mutations in the HA genes was 1.59 times higher than that of the NA gene (2.15/1.36) from the beginning of 2020 to 2022. The positively selective sites 174, 199, 214 and 563 in HA genes and the sites 73 and 384 in NA genes were evolutionarily selected in the 2021–2022 influenza outbreaks. Overall, the prevalent factors related to 2021–2022 influenza outbreaks included epidemic timing, Tv, Ts, Tv/Ts, P137 (B → P), P148 (B → P), P199 (P → A), P212 (P → A), P214 (H → P) and P563 (B → P). The preference of amino acid mutations for charge/pH could influence the epidemic/outbreak trends of infectious diseases. Here was a good model of the evolution of infectious disease pathogens. This study, on account of further exploration of virology, genetics, bioinformatics and outbreak information, might facilitate further understanding of their deep interaction mechanisms in the spread of infectious diseases.

Development of a Framework for Establishing 'Gold Standard' Outbreak Data from Submitted SARS-CoV-2 Genome Samples.

Submitted genomic data for respiratory viruses reflect the emergence and spread of new variants. Although delays in submission limit the utility of these data for prospective surveillance, they may be useful for evaluating other surveillance sources. However, few studies have investigated the use of these data for evaluating aberration detection in surveillance systems. Our study used a Bayesian online change point detection algorithm (BOCP) to detect increases in the number of submitted genome samples as a means of establishing 'gold standard' dates of outbreak onset in multiple countries. We compared models using different data transformations and parameter values. BOCP detected change points that were not sensitive to different parameter settings. We also found data transformations were essential prior to change point detection. Our study presents a framework for using global genomic submission data to develop 'gold standard' dates about the onset of outbreaks due to new viral variants.

Early warning COVID-19 outbreak in long-term care facilities using wastewater surveillance: correlation, prediction, and interaction with clinical and serological statuses

The unprecedented COVID-19 pandemic has highlighted the strategic value of wastewater-based surveillance (WBS) of SARS-CoV-2. This multisite 28-month-long study focused on WBS for older residents in 12long-term care facilities (LTCFs) in Edmonton (AB, Canada) by assessing relationships between COVID-19, WBS, and serostatus during the pandemic. Wastewater samples collected two to three times per week were tested for SARS-CoV-2 using RT-quantitative PCR. Theserostatus of antibodies was examined using immunoassays. The data of clinical COVID-19 outbreaks based on extensive testing were obtained from local public health officials. Analyses included calculating correlations between 7-day rolling averages for WBS and COVID-19 cases and investigating whether WBS led or lagged confirmed outbreaks using a multinomial test. Wastewater results correlated well with clinical COVID-19 infections and outbreaks at participating LTCFs. 1058 (36·0%) of 2936collected wastewater samples were SARS-CoV-2 positive, compared with 1247people (resident n=671, staff n=572, and unknown n=4) reporting positive test results of 21 673clinical samples assessed (5·8%). WBS led clinical testing in 32 (60·4%) confirmed outbreaks, which was significantly different from WBS lagged (12outbreaks [22·6%, 95% CI 11·3-33·7]). Non-detection of WBS SARS-CoV-2 served as a negative predictor for outbreaks. WBS results attested protective immunity in vaccinated individuals before the omicron wave. Aparallel increase in the proportions of positive WBS SARS-CoV-2 and anti-nucleocapsid antibodies underlined that omicron was an immunity-evading variant despite high seropositivity of neutralising antibodies after multiple doses of vaccine. Implementation of WBS could enable targeted clinical investigations and improve cost-effectiveness of COVID-19 outbreak management in LTCFs. WBS and serostatus provided informed dynamic changes of infections and immunity. Critical evidence was that LTCF WBS is an effective early warning system to support rapid public health outbreak management and protect vulnerable older populations. Canadian Immunity Task Force for COVID-19 and Alberta Health.

Estimating time-varying cholera transmission and oral cholera vaccine effectiveness in Haiti and Cameroon, 2021-2023.

In 2023, cholera affected approximately 1 million people and caused more than 5000 deaths globally, predominantly in low-income and conflict settings. In recent years, the number of new cholera outbreaks has grown rapidly. Further, ongoing cholera outbreaks have been exacerbated by conflict, climate change, and poor infrastructure, resulting in prolonged crises. As a result, the demand for treatment and intervention is quickly outpacing existing resource availability. Prior to improved water and sanitation systems, cholera, a disease primarily transmitted via contaminated water sources, also routinely ravaged high-income countries. Crumbling infrastructure and climate change are now putting new locations at risk - even in high-income countries. Thus, understanding the transmission and prevention of cholera is critical. Combating cholera requires multiple interventions, the two most common being behavioral education and water treatment. Two-dose oral cholera vaccination (OCV) is often used as a complement to these interventions. Due to limited supply, countries have recently switched to single-dose vaccines (OCV1). One challenge lies in understanding where to allocate OCV1 in a timely manner, especially in settings lacking well-resourced public health surveillance systems. As cholera occurs and propagates in such locations, timely, accurate, and openly accessible outbreak data are typically inaccessible for disease modeling and subsequent decision-making. In this study, we demonstrated the value of open-access data to rapidly estimate cholera transmission and vaccine effectiveness. Specifically, we obtained non-machine readable (NMR) epidemic curves for recent cholera outbreaks in two countries, Haiti and Cameroon, from figures published in situation and disease outbreak news reports. We used computational digitization techniques to derive weekly counts of cholera cases, resulting in nominal differences when compared against the reported cumulative case counts (i.e., a relative error rate of 5.67% in Haiti and 0.54% in Cameroon). Given these digitized time series, we leveraged EpiEstim-an open-source modeling platform-to derive rapid estimates of time-varying disease transmission via the effective reproduction number ( ). To compare OCV1 effectiveness in the two considered countries, we additionally used VaxEstim, a recent extension of EpiEstim that facilitates the estimation of vaccine effectiveness via the relation among three inputs: the basic reproduction number ( ), , and vaccine coverage. Here, with Haiti and Cameroon as case studies, we demonstrated the first implementation of VaxEstim in low-resource settings. Importantly, we are the first to use VaxEstim with digitized data rather than traditional epidemic surveillance data. In the initial phase of the outbreak, weekly rolling average estimates of were elevated in both countries: 2.60 in Haiti [95% credible interval: 2.42-2.79] and 1.90 in Cameroon [1.14-2.95]. These values are largely consistent with previous estimates of in Haiti, where average values have ranged from 1.06 to 3.72, and in Cameroon, where average values have ranged from 1.10 to 3.50. In both Haiti and Cameroon, this initial period of high transmission preceded a longer period during which oscillated around the critical threshold of 1. Our results derived from VaxEstim suggest that Haiti had higher OCV1 effectiveness than Cameroon (75.32% effective [54.00-86.39%] vs. 54.88% [18.94-84.90%]). These estimates of OCV1 effectiveness are generally aligned with those derived from field studies conducted in other countries. Thus, our case study reinforces the validity of VaxEstim as an alternative to costly, time-consuming field studies of OCV1 effectiveness. Indeed, prior work in South Sudan, Bangladesh, and the Democratic Republic of the Congo reported OCV1 effectiveness ranging from approximately 40% to 80%. This work underscores the value of combining NMR sources of outbreak case data with computational techniques and the utility of VaxEstim for rapid, inexpensive estimation of vaccine effectiveness in data-poor outbreak settings.

Genomic Characterization of Three Canadian Mumps Outbreaks Demonstrates Endemic Transmission in Canada.

Despite the provision of a mumps vaccination program in Canada for over three decades, mumps has not reached elimination. Instead, a re-emergence has been observed in vaccinated populations, particularly in young adults. These outbreaks have been almost exclusively due to genotype G infections, a trend that has been seen in other countries with high mumps vaccination rates. To characterize mumps outbreaks in Canada, genomes from samples from Manitoba (n = 209), Newfoundland (n = 25), and Nova Scotia (n = 48) were sequenced and analysed by Bayesian inference. Whole genome sequencing was shown to be highly discriminatory for outbreak investigations compared to traditional Sanger sequencing. The results showed that mumps virus genotype G most likely circulated endemically in Canada and between Canada and the US. Overall, this Canadian outbreak data from different provinces and ancestral strains demonstrates the benefits of molecular genomic data to better characterize mumps outbreaks, but also suggests genomics could further our understanding of the reasons for potential immune escape of mumps genotype G and evolution in highly vaccinated populations. With a possible endemic circulation of mumps genotype G and the remaining risk of new imported cases, increased surveillance and alternative vaccination strategies may be required for Canada to reach the current target for mumps or a future elimination status.

Surveillance of household foodborne disease outbreaks in Zhejiang Province, China, 2010–2022

BackgroundFoodborne diseases are a growing public health concern worldwide and households are a common setting. This study aimed to explore the epidemiological characteristics of household foodborne disease outbreaks in Zhejiang Province and propose targeted prevention and control measures. MethodsDescriptive statistical methods were used to analyze household foodborne disease outbreak data collected from the Foodborne Disease Outbreaks Surveillance System in Zhejiang Province from 2010 to 2022. ResultsHousehold foodborne disease outbreaks showed an upward trend during the study period (Cox-Staurt trend test, p = 0.01563 < 0.05). These outbreaks mainly occurred from June to September, with 62.08% (352/567) of all reported outbreaks. The number of reported outbreaks varied in 11 prefectures, with a maximum of 100 and a minimum of only 7. Household foodborne disease outbreaks had a wide spectrum of etiologic factors. Mushroom toxins accounted for the largest proportion of all etiologies (43.39 %) and caused the highest proportion of hospitalization (54.18%) and death (78.26%). Such outbreaks are caused by accidently eating wild poisonous mushrooms. Bacterial infection (16.23%) was the second most common etiology, with Salmonella spp. and Vibrio parahaemolyticus being the primary pathogens. These outbreaks were caused by improper storage, improper processing or a combination of factors, and the foods involved were mainly aquatic animals, eggs and cooked meat. Other identified etiologies included plant toxins (9.52%), chemicals (7.23%), animal toxins (3.70%), and viruses (1.76%). Among the above-mentioned etiologies, mushroom toxins, bacteria, and animal toxins had seasonal characteristics. Analysis of regions and etiologies revealed that the proportion of various etiologies was different in 11 prefectures. Wild mushrooms (43.39%), aquatic animals (9.88%), and toxic plants (8.47%) were the top three foods involved in these outbreaks. The most common factors contributing to household foodborne disease outbreaks were inedibility and misuse (59.08%), followed by multiple factors (7.58%), improper storage (7.41%), and improper processing (7.41%). ConclusionsHousehold foodborne disease outbreaks were closely related to the lack of knowledge regarding foodborne disease prevention. Therefore, public health agencies should strengthen residents’ surveillance and health education to improve food safety awareness and effectively reduce foodborne diseases in households. In addition, timely publicity and early warning by relevant government departments, the introduction of standards to control the contamination of pathogenic bacteria in raw materials, and strengthened supervision of the sale of substances that may cause health hazards, such as poisonous mushrooms and nitrites, will also help reduce such outbreaks.

SpatMCDA: An R package for assessing areas at risk of infectious diseases based on spatial multi‐criteria decision analysis

Abstract Effective visualization of infectious disease risks is crucial for the development of efficient prevention and control strategies. However, the efficacy of mainstream models is hindered by a scarcity of reliable data in target areas, a situation that is particularly acute when dealing with emerging or re‐emerging infectious diseases. Additionally, these models typically fail to integrate local disease‐related risk factors in line with the ‘One Health’ concept, resulting in inaccurate predictions. Consequently, accurately assessing infectious disease risks without reliable data is challenging. This study introduces SpatMCDA, an innovative R package designed to assess infectious disease risk areas through spatial multi‐criteria decision analysis (MCDA). SpatMCDA is structured around six core modelling steps: standardizing risk factors, determining factor weights, constructing risk maps, performing One‐at‐a‐Time sensitivity analysis, calculating the Mean of Absolute Change Rates and conducting an uncertainty analysis. By examining the case of West Nile virus (WNV) in China, this study illustrates how SpatMCDA can be useful in identifying disease transmission risks in the absence of reliable outbreak data. The assessment identified areas at risk for WNV in northwestern, eastern and southern China. By integrating spatial and epidemiological data, SpatMCDA enhances infectious diseases risk assessment in situations where data are limited. Its efficiency in using available data for accurate risk mapping and adaptability in weighting various risk factors enables tailored analyses. This tool enhances public health strategies, contributing to global health security.

ASPECTS OF FOOD SAFETY ISSUES IN MODERN AGRICULTURE

Foodborne illnesses remain a persistent public health threat, with thousands of outbreaks each year caused by contaminated produce, meat, and dairy. The study analyzed food safety risks associated with traditional industrial farming practices, including concentrated animal feeding operations (CAFOs), large-scale produce production, and nationwide processing and distribution networks. Quantitative analysis of outbreak data reveals hot spots of contamination in commodities and the food system. Qualitative research and interviews contextualize the complex contamination pathways and challenges of preventing the spread of pathogens in modern agriculture. The main risks include inappropriate manure use, contaminated irrigation water, undersized buffer zones and inadequate sanitation at processing facilities. Policy improvements related to livestock density, water treatment, and implementation of protection plans may help reduce future threats. Ultimately, enhancing food safety in a way that does not compromise productivity and affordability requires a comprehensive, systems-level approach across the farm, processing facility, and regulatory agency. KEYWORDS: food safety, foodborne illness, industrial agriculture, commodities, contamination, pathogens, policy

Integrating gated recurrent unit in graph neural network to improve infectious disease prediction: an attempt.

This study focuses on enhancing the precision of epidemic time series data prediction by integrating Gated Recurrent Unit (GRU) into a Graph Neural Network (GNN), forming the GRGNN. The accuracy of the GNN (Graph Neural Network) network with introduced GRU (Gated Recurrent Units) is validated by comparing it with seven commonly used prediction methods. The GRGNN methodology involves multivariate time series prediction using a GNN (Graph Neural Network) network improved by the integration of GRU (Gated Recurrent Units). Additionally, Graphical Fourier Transform (GFT) and Discrete Fourier Transform (DFT) are introduced. GFT captures inter-sequence correlations in the spectral domain, while DFT transforms data from the time domain to the frequency domain, revealing temporal node correlations. Following GFT and DFT, outbreak data are predicted through one-dimensional convolution and gated linear regression in the frequency domain, graph convolution in the spectral domain, and GRU (Gated Recurrent Units) in the time domain. The inverse transformation of GFT and DFT is employed, and final predictions are obtained after passing through a fully connected layer. Evaluation is conducted on three datasets: the COVID-19 datasets of 38 African countries and 42 European countries from worldometers, and the chickenpox dataset of 20 Hungarian regions from Kaggle. Metrics include Average Root Mean Square Error (ARMSE) and Average Mean Absolute Error (AMAE). For African COVID-19 dataset and Hungarian Chickenpox dataset, GRGNN consistently outperforms other methods in ARMSE and AMAE across various prediction step lengths. Optimal results are achieved even at extended prediction steps, highlighting the model's robustness. GRGNN proves effective in predicting epidemic time series data with high accuracy, demonstrating its potential in epidemic surveillance and early warning applications. However, further discussions and studies are warranted to refine its application and judgment methods, emphasizing the ongoing need for exploration and research in this domain.

DeepDynaForecast: Phylogenetic-informed graph deep learning for epidemic transmission dynamic prediction.

In the midst of an outbreak or sustained epidemic, reliable prediction of transmission risks and patterns of spread is critical to inform public health programs. Projections of transmission growth or decline among specific risk groups can aid in optimizing interventions, particularly when resources are limited. Phylogenetic trees have been widely used in the detection of transmission chains and high-risk populations. Moreover, tree topology and the incorporation of population parameters (phylodynamics) can be useful in reconstructing the evolutionary dynamics of an epidemic across space and time among individuals. We now demonstrate the utility of phylodynamic trees for transmission modeling and forecasting, developing a phylogeny-based deep learning system, referred to as DeepDynaForecast. Our approach leverages a primal-dual graph learning structure with shortcut multi-layer aggregation, which is suited for the early identification and prediction of transmission dynamics in emerging high-risk groups. We demonstrate the accuracy of DeepDynaForecast using simulated outbreak data and the utility of the learned model using empirical, large-scale data from the human immunodeficiency virus epidemic in Florida between 2012 and 2020. Our framework is available as open-source software (MIT license) at github.com/lab-smile/DeepDynaForcast.

Investigation of outbreaks and risk factors for brucellosis in goat and sheep farms in central Thailand

Background and Aim: Brucellosis is a harmful disease that affects a wide range of animals and adversely affects both animal and human health. Brucella melitensis is a zoonotic disease that causes brucellosis in sheep and goats. This study aimed to identify human index cases, identify risk factors for brucellosis in goat and sheep farms, and establish recommendations for farmers. Materials and Methods: An unmatched case-control study was conducted on goat or sheep farms in central Thailand. We utilized animal disease reports and outbreak investigation data extracted from the Department of Livestock Development, Thailand, and assessed these secondary data. A questionnaire was used to collect data. Serum samples were parallel tested for brucellosis using the modified Rose Bengal test and indirect enzyme-linked immunosorbent assay. Univariate and multivariate logistic regression analyses were performed. Results: An index case of human brucellosis was reported in the province of Chai Nat. Patients who were goat farmers or had previous experience of goat farming were included. Bacteria were introduced into the patient's farms by purchasing goats, grazing in public grassland, and insufficient biosecurity. Multivariable logistic regression analysis identified a higher risk of brucellosis positivity in the large herd than in the small herd (adjusted odds ratio = 8.61; 95% confidence interval = 1.62-45.71). Conclusion: These results suggest that goat or sheep farms with 50 heads should take further measures to prevent disease transmission, such as increasing the frequency of disease testing within the herd and strengthening the biosecurity system. Keywords: brucellosis, goat farms, outbreak investigation, risk factor, sheep farms.

IJCM_369A: Notification of malaria and dengue outbreaks during COVID-19 pandemic: A secondary analysis of weekly IDSP reporting

Background: Like many other diseases, vector borne diseases related health activities got affected during COVID-19 due to disruption in health services. The preventive measures, vector surveillance, diagnostics, early diagnosis, and management are likely to have been affected. Objective: To explore the vector-borne disease trends as outbreaks from January 2021 to December 2023 using weekly reports of Integrated Disease Surveillance Programme (IDSP). Methodology: We used the secondary data of the weekly reports of IDSP weekly reports from Jan 2020 Dec 2023. We included dengue and malaria as two important diseases to represent the prevailing vector borne diseases in the country. The outbreak data collected were entered in MS Excel and reported as frequency and proportions to visualize the time trends during the COVID-19 pandemic. Results: The outbreaks of malaria were found to occur mainly in the second half of the year with 63% and 80% outbreaks in the years 2020 and 2021 respectively, coinciding with the onset of monsoons. Maximum number of outbreaks was reported in the year of 2021, followed by 2022 with number of outbreaks being 20 and 19 respectively. Maximum number of dengue outbreaks were reported in the year 2021 followed by year 2022 with the numbers being 114 and 81 respectively. The year 2020 had around half the number of outbreaks as of the year 2022 (42 outbreaks). The first half of the year 2023 reported 25 outbreaks. Conclusion: Both malaria and dengue had maximum number of outbreaks in the year 2021 followed by a slight dip in the year 2022. At the onset of pandemic, the number of outbreaks were quite less compared to the subsequent years, probably due to the lockdown imposed.

Trends in Burdens of Disease by Transmission Source (USA, 2005–2020) and Hazard Identification for Foods: Focus on Milkborne Disease

BackgroundRobust solutions to global, national, and regional burdens of communicable and non-communicable diseases, particularly related to diet, demand interdisciplinary or transdisciplinary collaborations to effectively inform risk analysis and policy decisions.ObjectiveU.S. outbreak data for 2005–2020 from all transmission sources were analyzed for trends in the burden of infectious disease and foodborne outbreaks.MethodsOutbreak data from 58 Microsoft Access® data tables were structured using systematic queries and pivot tables for analysis by transmission source, pathogen, and date. Trends were examined using graphical representations, smoothing splines, Spearman’s rho rank correlations, and non-parametric testing for trend. Hazard Identification was conducted based on the number and severity of illnesses.ResultsThe evidence does not support increasing trends in the burden of infectious foodborne disease, though strongly increasing trends were observed for other transmission sources. Morbidity and mortality were dominated by person-to-person transmission; foodborne and other transmission sources accounted for small portions of the disease burden. Foods representing the greatest hazards associated with the four major foodborne bacterial diseases were identified. Fatal foodborne disease was dominated by fruits, vegetables, peanut butter, and pasteurized dairy.ConclusionThe available evidence conflicts with assumptions of zero risk for pasteurized milk and increasing trends in the burden of illness for raw milk. For future evidence-based risk management, transdisciplinary risk analysis methodologies are essential to balance both communicable and non-communicable diseases and both food safety and food security, considering scientific, sustainable, economic, cultural, social, and political factors to support health and wellness for humans and ecosystems.

Epidemiological characterization and risk assessment of rabbit haemorrhagic disease virus 2 (RHDV2/b/GI.2) in the world

A novel variant of rabbit haemorrhagic disease virus, designated RHDV2/b/GI.2, was first discovered in France in 2010. Subsequently, RHDV2 rapidly spread to Africa, North America, Australia, and Asia. RHDV2 outbreaks have resulted in significant economic losses in the global rabbit industry and disrupted the balance of natural ecosystems. Our study investigated the seasonal characteristics of RHDV2 outbreaks using seasonal indices. RHDV2 is prone to causing significant outbreaks within domestic and wild rabbit populations during the spring season and is more likely to induce outbreaks within wild rabbit populations during late autumn in the Southern Hemisphere. Furthermore, based on outbreak data for domestic and wild rabbits and environmental variables, our study established two MaxEnt models to explore the relationship between RHDV2 outbreaks and the environmental factors and conducted outbreak risk predictions for RHDV2 in global domestic and wild rabbit populations. Both models demonstrated good predictive performance, with AUC values of 0.960 and 0.974, respectively. Road density, isothermality, and population density were identified as important variables in the outbreak of RHDV2 in domestic rabbits, while road density, normalized difference vegetation index, and mean annual solar radiation were considered key variables in the outbreak of RHDV2 in wild rabbits. The environmental factors associated with RHDV2 outbreaks identified in our study and the outbreak risk prediction maps generated in our study will aid in the formulation of appropriate RHDV2 control measures to reduce the risk of morbidity in domestic and wild rabbits.