https://www.who.int/news/item/30-01-2020-statement-on-the-secondmeeting-of-the-international-health-regulations-( 2005)-emergency-committeeregarding-the-outbreak-of-novel-coronavirus-(2019-ncov) † https://www.who.int/publications/m/item/strategy-to-achieve-global-covid-19vaccination-by-mid-2022 § The strategy brief outlined updated goals, steps, targets, and operational priorities to guide countries, policy makers, civil society, manufacturers, and international organizations in their ongoing efforts through 2022. https://www.who.int/publications/m/item/ global-covid-19-vaccination-strategy-in-a-changing-world--july-2022-update ¶ Older adult definitions vary by country, ranging from persons aged ≥45 years to those aged ≥65 years.coverage with a complete COVID-19 vaccination series** for ** Definition of complete primary series might vary among countries and by vaccine product. National authorities have ultimate authority on scheduling decisions within their jurisdictions; however, WHO makes recommendations for COVID-19 vaccine products that have undergone Emergency Use Listing review. Vaccine fact sheets including these definitions according to WHO recommendations can be found at https://extranet.who.int/pqweb/vaccines/ vaccinescovid-19-vaccine-eul-issued.

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.

38Accurate knowledge of pathogen incubation period is essential to inform public health 39 policies and implement interventions that contribute to the reduction of burden of disease. 40The incubation period distribution of campylobacteriosis is currently unknown with several 41 sources reporting different times. Variation in the distribution could be expected due to host, 42 transmission vehicle, and organism characteristics, however, the extent of this variation and 43 influencing factors are unclear. 44The authors have undertaken a systematic review of published literature of outbreak studies 45 with well-defined point source exposures and human experimental studies to estimate the 46 distribution of incubation period and also identify and explain the variation in the distribution 47 between studies. We tested for heterogeneity using I 2 and Kolmogorov Smirnov tests, 48regressed incubation period against possible explanatory factors, and used hierarchical 49 clustering analysis to define subgroups of studies without evidence of heterogeneity. 50The mean incubation period of subgroups ranged from 2.5 to 4.3 days. We observed 51 variation in the distribution of incubation period between studies that was not due to chance. 52A significant association between the mean incubation period and age distribution was reported to the national surveillance system [8], and an estimated 280,000 cases reported 75 each year resulting in over 100 deaths [1,9]. 76A large proportion of reported cases are sporadic, however, outbreaks of campylobacteriosis 77have been reported with foodborne [10,11] and non-foodborne [12,13] 83Incubation period, which is the time between infection and onset of clinical symptoms, is also 84 important for surveillance and implementation of appropriate public health interventions. In 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 153The quality assessment undertaken in our review focused on assessing the quality of the 154 incubation period data reported based on a set of criteria developed by one of us (JIH) and155 not the quality of the overall study. This was done because many of the studies did not 156necessarily set out to study incubation period, but rather to report on the process of an 157 outbreak investigation or provide evidence on the source of infection in an outbreak. This 158 method of quality assessment enabled us to effectively evaluate the quality...

BackgroundSalmonella 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.MethodsIn 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.ResultsWe 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.ConclusionsWe 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.Electronic supplementary materialThe online version of this article (10.1186/s12879-018-3391-3) contains supplementary material, which is available to authorized users.

An outbreak of listeriosis in England affecting 14 people between 2010 and 2012 and linked to the consumption of pork pies was investigated. All 14 individuals were older than 55 years, 12 were men, and 10 reported the presence of an underlying condition. All were resident in or had visited either of two English regions and were infected with the same strain of Listeria monocytogenes. In interviews with 12 patients, 9 reported eating pork pies, and individuals that consumed pork pies were significantly more likely to be infected with an outbreak strain than were individuals with sporadic cases of listeriosis infections in England from 2010 to 2012. Pork pies were purchased from seven retailers in South Yorkshire or the East Midlands, and the outbreak strain was recovered from pork pies supplied by only the producer in South Yorkshire. The outbreak strain was also recovered from samples of finished product and from environmental samples collected from the manufacturer. The likely source of contamination was environmental sites within the manufacturing environment, and the contamination was associated with the process of adding gelatin to the pies after cooking. Inadequate temperature control and poor hygienic practices at one of the retailers were also identified as possible contributory factors allowing growth of the pathogen. Following improvements in manufacturing practices and implementation of additional control measures at the retailers' premises, L. monocytogenes was not recovered from subsequent food and environmental samples, and the outbreak strain was not detected in further individuals with listeriosis in England.

BackgroundListeriosis 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.MethodsEnhanced 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.ResultsThe 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).ConclusionsOur 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.Electronic supplementary materialThe online version of this article (doi:10.1186/s12879-016-1638-4) contains supplementary material, which is available to authorized users.

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.

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 I 2 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.