Informing the pandemic response: the role of the WHO's COVID-19 Weekly Epidemiological Update.

On 31 December 2019, the Municipal Health Commission of Wuhan, China, reported a cluster of atypical pneumonia cases. On 5 January 2020, the WHO publicly released a Disease Outbreak News (DON) report, providing information about the pneumonia cases, implemented response interventions, and WHO's risk assessment and advice on public health and social measures. Following 9 additional DON reports and 209 daily situation reports, on 17 August 2020, WHO published the first edition of the COVID-19 Weekly Epidemiological Update (WEU). On 1 September 2023, the 158th edition of the WEU was published on WHO's website, marking its final issue. Since then, the WEU has been replaced by comprehensive global epidemiological updates on COVID-19 released every 4 weeks. During the span of its publication, the webpage that hosts the WEU and the COVID-19 Operational Updates was accessed annually over 1.4 million times on average, with visits originating from more than 100 countries. This article provides an in-depth analysis of the WEU process, from data collection to publication, focusing on the scope, technical details, main features, underlying methods, impact and limitations. We also discuss WHO's experience in disseminating epidemiological information on the COVID-19 pandemic at the global level and provide recommendations for enhancing collaboration and information sharing to support future health emergency responses.

Disseminating information on acute public health events globally: experiences from the WHO's Disease Outbreak News.

WHO works, on a daily basis, with countries globally to detect, prepare for and respond to acute public health events. A vital component of a health response is the dissemination of accurate, reliable and authoritative information. The Disease Outbreak News (DON) reports are a key mechanism through which WHO communicates on acute public health events to the public. The decision to produce a DON report is taken on a case-by-case basis after evaluating key criteria, and the subsequent process of producing a DON report is highly standardised to ensure the robustness of information. DON reports have been published since 1996, and up to 2022 over 3000 reports have been published. Between 2018 and 2022, the most frequently published DON reports relate to Ebola virus disease, Middle East respiratory syndrome, yellow fever, polio and cholera. The DON web page is highly visited with a readership of over 2.6 million visits per year, on average. The DON report structure has evolved over time, from a single paragraph in 1996 to a detailed report with seven sections currently. WHO regularly reviews the DON report process and structure for improvements. In the last 25 years, DON reports have played a unique role in rapidly disseminating information on acute public health events to health actors and the public globally. They have become a key information source for the global public health response to the benefit of individuals and communities.

Global public health intelligence: World Health Organization operational practices.

Early warning and response are key to tackle emerging and acute public health risks globally. Therefore, the World Health Organization (WHO) has implemented a robust approach to public health intelligence (PHI) for the global detection, verification and risk assessment of acute public health threats. WHO's PHI operations are underpinned by the International Health Regulations (2005), which require that countries strengthen surveillance efforts, and assess, notify and verify events that may constitute a public health emergency of international concern (PHEIC). PHI activities at WHO are conducted systematically at WHO's headquarters and all six regional offices continuously, throughout every day of the year. We describe four interlinked steps; detection, verification, risk assessment, and reporting and dissemination. For PHI operations, a diverse and interdisciplinary workforce is needed. Overall, PHI is a key feature of the global health architecture and will only become more prominent as the world faces increasing public health threats.

COVID-19 Mortality and Progress Toward Vaccinating Older Adults - World Health Organization, Worldwide, 2020-2022.

After the emergence of SARS-CoV-2 in late 2019, transmission expanded globally, and on January 30, 2020, COVID-19 was declared a public health emergency of international concern.* Analysis of the early Wuhan, China outbreak (1), subsequently confirmed by multiple other studies (2,3), found that 80% of deaths occurred among persons aged ≥60 years. In anticipation of the time needed for the global vaccine supply to meet all needs, the World Health Organization (WHO) published the Strategic Advisory Group of Experts on Immunization (SAGE) Values Framework and a roadmap for prioritizing use of COVID-19 vaccines in late 2020 (4,5), followed by a strategy brief to outline urgent actions in October 2021.† WHO described the general principles, objectives, and priorities needed to support country planning of vaccine rollout to minimize severe disease and death. A July 2022 update to the strategy brief§ prioritized vaccination of populations at increased risk, including older adults,¶ with the goal of 100% coverage with a complete COVID-19 vaccination series** for at-risk populations. Using available public data on COVID-19 mortality (reported deaths and model estimates) for 2020 and 2021 and the most recent reported COVID-19 vaccination coverage data from WHO, investigators performed descriptive analyses to examine age-specific mortality and global vaccination rollout among older adults (as defined by each country), stratified by country World Bank income status. Data quality and COVID-19 death reporting frequency varied by data source; however, persons aged ≥60 years accounted for >80% of the overall COVID-19 mortality across all income groups, with upper- and lower-middle-income countries accounting for 80% of the overall estimated excess mortality. Effective COVID-19 vaccines were authorized for use in December 2020, with global supply scaled up sufficiently to meet country needs by late 2021 (6). COVID-19 vaccines are safe and highly effective in reducing severe COVID-19, hospitalizations, and mortality (7,8); nevertheless, country-reported median completed primary series coverage among adults aged ≥60 years only reached 76% by the end of 2022, substantially below the WHO goal, especially in middle- and low-income countries. Increased efforts are needed to increase primary series and booster dose coverage among all older adults as recommended by WHO and national health authorities.

Incubation Period of Shiga Toxin-Producing Escherichia coli.

Shiga toxin-producing Escherichia coli are pathogenic bacteria found in the gastrointestinal tract of humans. Severe infections could lead to life-threatening complications, especially in young children and the elderly. Understanding the distribution of the incubation period, which is currently inconsistent and ambiguous, can help in controlling the burden of disease. We conducted a systematic review of outbreak investigation reports, extracted individual incubation data and summary estimates, tested for heterogeneity, classified studies into subgroups with limited heterogeneity, and undertook a meta-analysis to identify factors that may contribute to the distribution of the pathogen's incubation period. Twenty-eight studies were identified for inclusion in the review (1 of which included information on 2 outbreaks), and the resulting I2 value was 77%, indicating high heterogeneity. Studies were classified into 5 subgroups, with the mean incubation period ranging from 3.5 to 8.1 days. The length of the incubation period increased with patient age and decreased by 7.2 hours with every 10% increase in attack rate.

Within-host mathematical modelling of the incubation period of Salmonella Typhi.

Mechanistic mathematical models are often employed to understand the dynamics of infectious diseases within a population or within a host. They provide estimates that may not be otherwise available. We have developed a within-host mathematical model in order to understand how the pathophysiology of Salmonella Typhi contributes to its incubation period. The model describes the process of infection from ingestion to the onset of clinical illness using a set of ordinary differential equations. The model was parametrized using estimated values from human and mouse experimental studies and the incubation period was estimated as 9.6 days. A sensitivity analysis was also conducted to identify the parameters that most affect the derived incubation period. The migration of bacteria to the caecal lymph node was observed as a major bottle neck for infection. The sensitivity analysis indicated the growth rate of bacteria in late phase systemic infection and the net population of bacteria in the colon as parameters that most influence the incubation period. We have shown in this study how mathematical models aid in the understanding of biological processes and can be used in estimating parameters of infectious diseases.

Utility of Whole Genome Sequencing To Describe the Persistence and Evolution of Listeria monocytogenes Strains within Crabmeat Processing Environments Linked to Two Outbreaks of Listeriosis.

This article describes the identification and investigation of two extended outbreaks of listeriosis in which crabmeat was identified as the vehicle of infection. Comparing contemporary and retrospective typing data of Listeria monocytogenes isolates from clinical cases and from food and food processing environments allowed the detection of cases going back several years. This information, combined with the analysis of routinely collected enhanced surveillance data, helped to direct the investigation and identify the vehicle of infection. Retrospective whole genome sequencing (WGS) analysis of isolates provided robust microbiological evidence of links between cases, foods, and the environments in which they were produced and demonstrated that for some cases and foods, identified by fluorescent amplified fragment length polymorphism, the molecular typing method in routine use at the time, were not part of the outbreak. WGS analysis also showed that the strains causing illness had persisted in two food production environments for many years and in one producer had evolved into two strains over a period of around 8 years. This article demonstrates the value of reviewing L. monocytogenes typing data from clinical cases together with that from foods as a means of identifying potential vehicles and sources of infection in outbreaks of listeriosis. It illustrates the importance of reviewing retrospective L. monocytogenes typing alongside enhanced surveillance data to characterize extended outbreaks and inform control measures. Also, this article highlights the advantages of WGS analysis for strain discrimination and clarification of evolutionary relationships that refine outbreak investigations and improve our understanding of L. monocytogenes in the food chain.

Incubation period of typhoidal salmonellosis: a systematic review and meta-analysis of outbreaks and experimental studies occurring over the last century.

BACKGROUND: Salmonella Typhi is a human pathogen that causes typhoid fever. It is a major cause of morbidity and mortality in developing countries and is responsible for several outbreaks in developed countries. Studying certain parameters of the pathogen, such as the incubation period, provides a better understanding of its pathophysiology and its characteristics within a population. Outbreak investigations and human experimental studies provide an avenue to study these relevant parameters. METHODS: In this study, the authors have undertaken a systematic review of outbreak investigation reports and experimental studies, extracted reported data, tested for heterogeneity, identified subgroups of studies with limited evidence of heterogeneity between them and identified factors that may contribute to the distribution of incubation period. Following identification of relevant studies, we extracted both raw and summary incubation data. We tested for heterogeneity by deriving the value of I2 and conducting a KS-test to compare the distribution between studies. We performed a linear regression analysis to identify the factors associated with incubation period and using the resulting p-values from the KS-test, we conducted a hierarchical cluster analysis to classify studies with limited evidence of heterogeneity into subgroups. RESULTS: We identified thirteen studies to be included in the review and extracted raw incubation period data from eleven. The value of I2 was 84% and the proportion of KS test p-values that were less than 0.05 was 63.6% indicating high heterogeneity not due to chance. We identified vaccine history and attack rates as factors that may be associated with incubation period, although these were not significant in the multivariable analysis (p-value: 0.1). From the hierarchical clustering analysis, we classified the studies into five subgroups. The mean incubation period of the subgroups ranged from 9.7 days to 21.2 days. Outbreaks reporting cases with previous vaccination history were clustered in a single subgroup and reported the longest incubation period. CONCLUSIONS: We identified attack rate and previous vaccination as possible associating factors, however further work involving analyses of individual patient data and developing mathematical models is needed to confirm these as well as examine additional factors that have not been included in our study.

Taste and Safety: Is the Exceptional Cuisine Offered by High End Restaurants Paralleled by High Standards of Food Safety?

INTRODUCTION: Restaurant guides such as the Good Food Guide Top 50 create a hierarchy focussing on taste and sophistication. Safety is not explicitly included. We used restaurant associated outbreaks to assess evidence for safety. METHODS: All foodborne disease outbreaks in England reported to the national database from 2000 to 2014 were used to compare the Top 50 restaurants (2015) to other registered food businesses using the Public Health England (PHE) outbreak database. Health Protection Teams were also contacted to identify any outbreaks not reported to the national database. Among Good Food Guide Top 50 restaurants, regression analysis estimated the association between outbreak occurrence and position on the list. RESULTS: Four outbreaks were reported to the PHE national outbreak database among the Top 50 giving a rate 39 times higher (95% CI 14.5-103.2) than other registered food businesses. Eight outbreaks among the 44 English restaurants in the Top 50 were identified by direct contact with local Health Protection Teams. For every ten places higher ranked, Top 50 restaurants were 66% more likely to have an outbreak (Odds Ratio 1.66, 95% CI 0.89-3.13). DISCUSSION: Top 50 restaurants were substantially more likely to have had reported outbreaks from 2000-2014 than other food premises, and there was a trend for higher rating position to be associated with higher probability of reported outbreaks. Our findings, that eating at some of these restaurants may pose an increased risk to health compared to other dining out, raises the question of whether food guides should consider aspects of food safety alongside the clearly important complementary focus on taste and other aspects of the dining experience.

Factors influencing the time between onset of illness and specimen collection in the diagnosis of non-pregnancy associated listeriosis in England and Wales.

BACKGROUND: Listeriosis is an opportunistic bacterial infection caused by Listeria monocytogenes and predominantly affects people who are immunocompromised. Due to its severity and the population at risk, prompt clinical diagnosis and treatment of listeriosis is essential. A major step to making a clinical diagnosis is the collection of the appropriate specimen(s) for testing. This study explores factors that may influence the time between onset of illness and collection of specimen in order to inform clinical policy and develop necessary interventions. METHODS: Enhanced surveillance data on non-pregnancy associated listeriosis in England and Wales between 2004 and 2013 were collected and analysed. The difference in days between onset of symptoms and collection of specimen was calculated and factors influencing the time difference were identified using a gamma regression model. RESULTS: The median number of days between onset of symptoms and collection of specimen was two days with 27.1 % of cases reporting one day between onset of symptoms and collection of specimen and 18.8 % of cases reporting more than seven days before collection of specimen. The median number of days between onset of symptoms and collection of specimen was shorter for cases infected with Listeria monocytogenes serogroup 1/2b (one day) and cases with an underlying condition (one day) compared with cases infected with serotype 4 (two days) and cases without underlying conditions (two days). CONCLUSIONS: Our study has shown that Listeria monocytogenes serotype and the presence of an underlying condition may influence the time between onset of symptoms and collection of specimen.