Middle East respiratory syndrome coronavirus (MERS-CoV) remains a notable disease and poses a significant threat to global public health. The Arabian Peninsula is considered a major global epicentre for the disease and the virus has crossed regional and continental boundaries since 2012. In this study, we focused on exploring the temporal dynamics of MERS-CoV in human populations in the Arabian Peninsula between 2012 and 2017, using publicly available data on case counts and combining two analytical methods. Disease progression was assessed by quantifying the time-dependent reproductive number (TD-Rs), while case series temporal pattern was modelled using the AutoRegressive Integrated Moving Average (ARIMA). We accounted for geographical variability between three major affected regions in Saudi Arabia including Eastern Province, Riyadh and Makkah. In Saudi Arabia, the epidemic size was large with TD-Rs >1, indicating significant spread until 2017. In both Makkah and Riyadh regions, the epidemic progression reached its peak in April 2014 (TD-Rs > 7), during the highest incidence period of MERS-CoV cases. In Eastern Province, one unique super-spreading event (TD-R > 10) was identified in May 2013, which comprised of the most notable cases of human-to-human transmission. Best-fitting ARIMA model inferred statistically significant biannual seasonality in Riyadh region, a region characterised by heavy seasonal camel-related activities. However, no statistical evidence of seasonality was identified in Eastern Province and Makkah. Instead, both areas were marked by an endemic pattern of cases with sporadic outbreaks. Our study suggested new insights into the epidemiology of the virus, including inferences about epidemic progression and evidence for seasonality. Despite the inherent limitations of the available data, our conclusions provide further guidance to currently implement risk-based surveillance in high-risk populations and, subsequently, improve related interventions strategies against the epidemic at country and regional levels.

Previously, we reported a phylogenetic study of 98 Burkholderia pseudomallei clinical isolates from Hainan, China. Here, we update the B. pseudomallei strain library with 52 strains from newly identified cases dating from 2014 to 2017, analysed by multilocus sequence typing. Twenty-two sequence types (STs) were identified from the 52 cases, illustrating high genetic diversity; five of them (ST1480, ST1481, ST1482, ST1483 and ST1484) were novel. ST46, ST50 and ST58 predominated (34.6%) as was the case in the previous study (35.7%). An e-BURST map of the ST profiles of the two collections of isolates showed their genetic foundation to be largely unchanged. Neighbour-joining tree analysis was suggestive of a close phylogenetic relationship between the novel STs from this series and those first reported from Hainan (ST1105, ST1099, ST55 and ST1095). Moreover, the two novel STs (1481 and 1483) showed close similarity to ST58 which originated in Thailand indicating a close relationship between B. pseudomallei strains from both countries. The previously described allele profiles gmhD-36 and lepA-68 were found for the first time in our strain collections. Our study emphasises the importance of monitoring the epidemiological status and evolutionary trends of B. pseudomallei in China.

Measles is a notifiable disease, but not everyone infected seeks care, nor is every consultation reported. We estimated the completeness of reporting during a measles outbreak in The Netherlands in 2013–2014. Children below 15 years of age in a low vaccination coverage community (n = 3422) received a questionnaire to identify measles cases. Cases found in the survey were matched with the register of notifiable diseases to estimate the completeness of reporting. Second, completeness of reporting was assessed by comparing the number of susceptible individuals prior to the outbreak with the number of reported cases in the surveyed community and on a national level.

We found 307 (15%) self-identified measles cases among 2077 returned questionnaires (61%), of which 27 could be matched to a case reported to the national register; completeness of reporting was 8.8%. Based on the number of susceptible individuals and number of reported cases in the surveyed community and on national level, the completeness of reporting was estimated to be 9.1% and 8.6%, respectively. Estimating the completeness of reporting gave almost identical estimates, which lends support to the credibility and validity of both approaches. The size of the 2013–2014 outbreak approximated 31 400 measles infections.

In 2017, Public Health England South East Health Protection Team (HPT) were involved in the management of an outbreak of Mycobacterium bovis (the causative agent of bovine tuberculosis) in a pack of working foxhounds. This paper summarises the actions taken by the team in managing the public health aspects of the outbreak, and lessons learned to improve the management of future potential outbreaks. A literature search was conducted to identify relevant publications on M. bovis. Clinical notes from the Public Health England (PHE) health protection database were reviewed and key points extracted. Animal and public health stakeholders involved in the management of the situation provided further evidence through unstructured interviews and personal communications. The PHE South East team initially provided ‘inform and advise’ letters to human contacts whilst awaiting laboratory confirmation to identify the infectious agent. Once M. bovis had been confirmed in the hounds, an in-depth risk assessment was conducted, and contacts were stratified in to risk pools. Eleven out of 20 exposed persons with the greatest risk of exposure were recommended to attend TB screening and one tested positive, but had no evidence of active TB infection. The number of human contacts working with foxhound packs can be large and varied. HPTs should undertake a comprehensive risk assessment of all potential routes of exposure, involve all other relevant stakeholders from an early stage and undertake regular risk assessments. Current guidance should be revised to account for the unique risks to human health posed by exposure to infected working dogs.

At present, the number of people with tuberculosis in China is second only to India and ranks second in the world. Under such a severe case of tuberculosis in China, prevention and control of pulmonary tuberculosis are urgently needed. This study aimed to study the temporal and geographical relevance of the pathogenesis of pulmonary tuberculosis and the factors affecting the incidence of tuberculosis. Spatial autocorrelation model was used to study the spatial distribution characteristics of pulmonary tuberculosis from a quantitative level. The research results showed that the overall incidence of pulmonary tuberculosis (IPT) in China was low in the east, high in the west and had certain seasonal characteristics. We use Spatial Lag Model to explore influencing factors of pulmonary tuberculosis. It indicates that the IPT is high in areas with underdeveloped economics, poor social services and low average smoking ages. Additionally, the IPT is high in areas with high AIDS prevalence. Also, compared with Classical Regression Model and Spatial Error Model, our model has smaller values of Akaike information criterion and Schwarz criterion. Besides, our model has bigger values of coefficient of determination (R2) and log-likelihood (log L) than the other two models. Apart from that, it is more significant than Spatial Error Models in the spatial dependence test for the IPT.

Hepatitis E virus (HEV) infection is endemic in Cambodia. However, little relevant data were available and there is no clue if HEV is an emerging or decreasing pathogen in that setting. The aim of our study was to describe temporal trends of anti-HEV IgG and IgM prevalences during the last two decades (1996–2017) in the context of population growth and urbanisation in Cambodia. A total of 2004 human plasma samples collected between 1996 and 2017 were tested for anti-HEV IgG and IgM using the commercial Wantai anti-HEV assays. Overall, the prevalences of anti-HEV IgG and IgM were 41.1% and 2.7%, respectively. Analysis by calendar period showed a decreasing trend of anti-HEV IgG prevalence over the last 21 years. After age- and gender-standardisation, the anti-HEV IgG prevalence rates decreased from 61.3% during the 1996–2000 period to 32.3% during the 2016–2017 period, but no trends were observed for anti-HEV IgM rates, which fluctuated around the overall one. In conclusion, our results suggest that HEV is not an emerging pathogen, but rather seems to circulate less in Cambodia, in particular, in Phnom Penh, since the prevalence of anti-HEV IgG has been significantly decreased during the past two decades.

We assess the relationship between epidemic size and the scaling of epidemic growth of Ebola epidemics at the level of administrative areas during the 2014–16 Ebola epidemic in West Africa. For this purpose, we quantify growth scaling parameters from the ascending phase of Ebola outbreaks comprising at least 7 weeks of epidemic growth. We then study how these parameters are associated with observed epidemic sizes. For validation purposes, we also analyse two historic Ebola outbreaks. We find a high monotonic association between the scaling of epidemic growth parameter and the observed epidemic size. For example, scaling of growth parameters around 0.3–0.4, 0.4–0.6 and 0.6 are associated with epidemic sizes on the order of 350–460, 460–840 and 840–2500 cases, respectively. These results are not explained by differences in epidemic onset across affected areas. We also find the relationship between the scaling of epidemic growth parameter and the observed epidemic size to be consistent for two past Ebola outbreaks in Congo (1976) and Uganda (2000). Signature features of epidemic growth could become useful to assess the risk of observing a major epidemic outbreak, generate improved diseases forecasts and enhance the predictive power of epidemic models. Our results indicate that the epidemic growth scaling parameter is a useful indicator of epidemic size, which may have significant implications to guide control of Ebola outbreaks and possibly other infectious diseases.

During the summer of 2016, the Hawaii Department of Health responded to the second-largest domestic foodborne hepatitis A virus (HAV) outbreak in the post-vaccine era. The epidemiological investigation included case finding and investigation, sequencing of RNA positive clinical specimens, product trace-back and virologic testing and sequencing of HAV RNA from the product. Additionally, an online survey open to all Hawaii residents was conducted to estimate baseline commercial food consumption. We identified 292 confirmed HAV cases, of whom 11 (4%) were possible secondary cases. Seventy-four (25%) were hospitalised and there were two deaths. Among all cases, 94% reported eating at Oahu or Kauai Island branches of Restaurant Chain A, with 86% of those cases reporting raw scallop consumption. In contrast, a food consumption survey conducted during the outbreak indicated 25% of Oahu residents patronised Restaurant Chain A in the 7 weeks before the survey. Product trace-back revealed a single distributor that supplied scallops imported from the Philippines to Restaurant Chain A. Recovery, amplification and sequence comparison of HAV recovered from scallops revealed viral sequences matching those from case-patients. Removal of product from implicated restaurants and vaccination of those potentially exposed led to the cessation of the outbreak. This outbreak further highlights the need for improved imported food safety.

Legionnaires’ disease (LD) incidence in the USA has quadrupled since 2000. Health departments must detect LD outbreaks quickly to identify and remediate sources. We tested the performance of a system to prospectively detect simulated LD outbreaks in Allegheny County, Pennsylvania, USA. We generated three simulated LD outbreaks based on published outbreaks. After verifying no significant clusters existed in surveillance data during 2014–2016, we embedded simulated outbreak-associated cases into 2016, assigning simulated residences and report dates. We mimicked daily analyses in 2016 using the prospective space-time permutation scan statistic to detect clusters of ⩽30 and ⩽180 days using 365-day and 730-day baseline periods, respectively. We used recurrence interval (RI) thresholds of ⩾20, ⩾100 and ⩾365 days to define significant signals. We calculated sensitivity, specificity and positive and negative predictive values for daily analyses, separately for each embedded outbreak. Two large, simulated cooling tower-associated outbreaks were detected. As the RI threshold was increased, sensitivity and negative predictive value decreased, while positive predictive value and specificity increased. A small, simulated potable water-associated outbreak was not detected. Use of a RI threshold of ⩾100 days minimised time-to-detection while maximizing positive predictive value. Health departments should consider using this system to detect community-acquired LD outbreaks.

Due to differences in the circulation of influenza viruses, distribution and antigenic drift of A subtypes and B lineages, and susceptibility to infection in the population, the incidence of symptomatic influenza infection can vary widely between seasons and age-groups. Our goal was to estimate the symptomatic infection incidence in the Netherlands for the six seasons 2011/2012 through 2016/2017, using Bayesian evidence synthesis methodology to combine season-specific sentinel surveillance data on influenza-like illness (ILI), virus detections in sampled ILI cases and data on healthcare-seeking behaviour. Estimated age-aggregated incidence was 6.5 per 1000 persons (95% uncertainty interval (UI): 4.7–9.0) for season 2011/2012, 36.7 (95% UI: 31.2–42.8) for 2012/2013, 9.1 (95% UI: 6.3–12.9) for 2013/2014, 41.1 (95% UI: 35.0–47.7) for 2014/2015, 39.4 (95% UI: 33.4–46.1) for 2015/2016 and 27.8 (95% UI: 22.7–33.7) for season 2016/2017. Incidence varied substantially between age-groups (highest for the age-group <5 years: 23 to 47/1000, but relatively low for 65+ years: 2 to 34/1000 over the six seasons). Integration of all relevant data sources within an evidence synthesis framework has allowed the estimation – with appropriately quantified uncertainty – of the incidence of symptomatic influenza virus infection. These estimates provide valuable insight into the variation in influenza epidemics across seasons, by virus subtype and lineage, and between age-groups.

Healthcare-associated infections (HAIs) are a major worldwide public-health problem, but less data are available on the long-term trends of HAIs and antimicrobial use in Eastern China. This study describes the prevalence and long-term trends of HAIs and antimicrobial use in a tertiary care teaching hospital in Hefei, Anhui, China from 2010 to 2017 based on annual point-prevalence surveys. A total of 12 505 inpatients were included; 600 HAIs were recorded in 533 patients, with an overall prevalence of 4.26% and a frequency of 4.80%. No evidence was found for an increasing or decreasing trend in prevalence of HAI over 8 years (trend χ2 = 2.15, P = 0.143). However, significant differences in prevalence of HAI were evident between the surveys (χ2 = 21.14, P < 0.001). The intensive care unit had the highest frequency of HAIs (24.36%) and respiratory tract infections accounted for 62.50% of all cases; Escherichia coli was the most common pathogen (16.67%). A 44.13% prevalence of antimicrobial use with a gradually decreasing trend over time was recorded. More attention should be paid to potential high-risk clinical departments and HAI types with further enhancement of rational antimicrobial use.

Dengue fever (DF) has been a growing public-health concern in China since its emergence in Guangdong Province in 1978. Of all the regions that have experienced dengue outbreaks in mainland China, the city of Guangzhou is the most affected. This study aims to investigate the potential risk factors for dengue virus (DENV) transmission in Guangzhou, China, from 2006 to 2014. The impact of risk factors on DENV transmission was qualified by the q-values calculated using a novel spatial-temporal method, the GeoDetector model. Both climatic and socioeconomic factors were considered. The impacts on DF incidence of each single factor and the interaction of two factors were analysed. The results show that the number of days with rainfall of the month before last has the highest determinant power, with a q-value of 0.898 (P < 0.01); the q-values of the other factors related to temperature and precipitation were around 0.38–0.50. Integrating a Pearson correlation analysis, nonlinear associations were found between the DF incidence in Guangzhou and the climatic factors considered. The coupled impact of the different variables considered was enhanced compared with their individual effects. In addition, an increased number of tourists in the city were associated with a high incidence of DF. This study demonstrates that the number of rain days in a month has great influence on the DF incidence of the month after next; the temperature and precipitation have nonlinear impacts on the DF incidence in Guangzhou; both the domestic and overseas tourists coming to the city increase the risk of DENV transmission. These findings are useful in the risk assessment of DENV transmission, to predict DF outbreaks and to implement preventive DF reduction strategies.

Dengue fever (DF) is one of the world's most disabling mosquito-borne diseases, with a variety of approaches available to model its spatial and temporal dynamics. This paper aims to identify and compare the different spatial and spatio-temporal Bayesian modelling methods that have been applied to DF and examine influential covariates that have been reportedly associated with the risk of DF. A systematic search was performed in December 2017, using Web of Science, Scopus, ScienceDirect, PubMed, ProQuest and Medline (via Ebscohost) electronic databases. The search was restricted to refereed journal articles published in English from January 2000 to November 2017. Thirty-one articles met the inclusion criteria. Using a modified quality assessment tool, the median quality score across studies was 14/16. The most popular Bayesian statistical approach to dengue modelling was a generalised linear mixed model with spatial random effects described by a conditional autoregressive prior. A limited number of studies included spatio-temporal random effects. Temperature and precipitation were shown to often influence the risk of dengue. Developing spatio-temporal random-effect models, considering other priors, using a dataset that covers an extended time period, and investigating other covariates would help to better understand and control DF transmission.

A growing number of infectious pathogens are spreading among geographic regions. Some pathogens that were previously not considered to pose a general threat to human health have emerged at regional and global scales, such as Zika and Ebola Virus Disease. Other pathogens, such as yellow fever virus, were previously thought to be under control but have recently re-emerged, causing new challenges to public health organisations. A wide array of new modelling techniques, aided by increased computing capabilities, novel diagnostic tools, and the increased speed and availability of genomic sequencing allow researchers to identify new pathogens more rapidly, assess the likelihood of geographic spread, and quantify the speed of human-to-human transmission. Despite some initial successes in predicting the spread of acute viral infections, the practicalities and sustainability of such approaches will need to be evaluated in the context of public health responses.

The Middle East respiratory syndrome coronavirus (MERS-CoV) is a life-threatening respiratory disease with a high case fatality rate; however, its risk factors remain unclear. We aimed to explore the influence of demographic factors, clinical manifestations and underlying comorbidities on mortality in MERS-CoV patients. Retrospective chart reviews were performed to identify all laboratory-confirmed cases of MERS-COV infection in Saudi Arabia that were reported to the Ministry of Health of Saudi Arabia between 23 April 2014 and 7 June 2016. Statistical analyses were conducted to assess the effect of sex, age, clinical presentation and comorbidities on mortality from MERS-CoV. A total of 281 confirmed MERS-CoV cases were identified: 167 (59.4%) patients were male and 55 (20%) died. Mortality predominantly occurred among Saudi nationals and older patients and was significantly associated with respiratory failure and shortness of breath. Of the 281 confirmed cases, 160 (56.9%) involved comorbidities, wherein diabetes mellitus, hypertension, ischemic heart disease, congestive heart failure, end-stage renal disease and chronic kidney disease were significantly associated with mortality from MERS-CoV and two or three comorbidities significantly affected the fatality rates from MERS-CoV. The findings of this study show that old age and the existence of underlying comorbidities significantly increase mortality from MERS-CoV.

Toxoplasma gondii (T. gondii) as an obligate intracellular protozoan with a worldwide distribution can infect virtually all warm-blooded animals and humans. This study aims to provide a summary of the available data on genotypes of T. gondii in human. Five databases including MEDLINE in PubMed, Scopus, Science Direct, Web of Science and Google Scholar were searched for the T. gondii genotyping in human during 1995–August 2017. Next, we screened all the articles based on the inclusion and exclusion criteria. Overall, 26 studies were eligible regarding genotyping T. gondii in human samples. In clonal genotyping, 167 out of 286 cases (58%) were infected with type II. Genetic characterisation of T. gondii isolates displayed that type II was the most predominant genotype in human with the prevalence of 64.3%, 62.1% and 41.7% in patients with AIDS, congenital and ocular toxoplasmosis, respectively. In ToxoDB genotyping, most individuals were infected with genotypes #9 and #65 (21.2%). Based on these results, genotype profile of T. gondii isolates is different throughout the world. The strains in Asian and African countries are characterised by low genetic diversity, while in North and South America a wide diversity of this parasite is found. In countries without any data (e.g. Australia, Western and Southern Africa and Western Asia), identification of T. gondii genotypes might discover higher genetic diversity.

Atherosclerotic changes can be measured as changes in common carotid intima media thickness (CIMT). It is hypothesised that repeated infection-associated inflammatory responses in childhood contribute to the atherosclerotic process. We set out to determine whether the frequency of infectious diseases in childhood is associated with CIMT in adolescence. The study is part of the Prevention and Incidence of Asthma and Mite Allergy (PIAMA) population-based birth cohort. At age 16 years, common CIMT was measured. We collected general practitioner (GP) diagnosed infections and prescribed antibiotics. Parent-reported infections were retrieved from annual questionnaires. Linear regression analysis assessed the association between number of infections during the first 4 years of life and common CIMT. Common CIMT measurement, GP and questionnaire data were available for 221 participants. No association was observed between the infection measures and CIMT. In a subgroup analysis, significant positive associations with CIMT were observed in participants with low parental education for 2–3 or ⩾7 GP diagnosed infections (+26.4 µm, 95% CI 0.4–52.4 and +26.8 µm, 95% CI 3.6–49.9, respectively) and ⩾3 antibiotic prescriptions (+35.5 µm, 95%CI 15.8–55.3). Overall, early childhood infections were not associated with common CIMT in adolescence. However, a higher number of childhood infections might contribute to the inflammatory process of atherosclerosis in subgroups with low education, this needs to be confirmed in future studies.

Japan is still a medium-burden tuberculosis (TB) country. We aimed to examine trends in newly notified active TB incidence and TB-related mortality in the last two decades in Japan. This is a population-based study using Japanese Vital Statistics and Japan Tuberculosis Surveillance from 1997 to 2016. We determined active TB incidence and mortality rates (per 100 000 population) by sex, age and disease categories. Joinpoint regression was applied to calculate the annual percentage change (APC) in age-adjusted mortality rates and to identify the years showing significant trend changes. Crude and age-adjusted incidence rates reduced from 33.9 to 13.9 and 37.3 to 11.3 per 100 000 population, respectively. Also, crude and age-adjusted mortality rates reduced from 2.2 to 1.5 and 2.8 to 1.0 per 100 000 population, respectively. Average APC in the incidence and mortality rates showed significant decline both in men (−6.2% and −5.4%, respectively) and women (−5.7% and −4.6%, respectively). Age-specific analysis demonstrated decreases in incidence and mortality rates for every age category, except for the incidence trend in the younger population. Although trends in active TB incidence and mortality rates in Japan have favourably decreased, the rate of decline is far from achieving TB elimination by 2035.

In several lately published studies, the association between single-nucleotide polymorphism (SNP, rs12252) of IFITM3 and the risk of influenza is inconsistent. To further understand the association between the SNP of IFITM3 and the risk of influenza, we searched related studies in five databases including PubMed published earlier than 9 November 2017. Ten sets of data from nine studies were included and data were analysed by Revman 5.0 and Stata 12.0 in our updated meta-analysis, which represented 1365 patients and 5425 no-influenza controls from four different ethnicities. Here strong association between rs12252 and influenza was found in all four genetic models. The significant differences in the allelic model (C vs. T: odds ratio (OR) = 1.35, 95% confidence interval (CI) (1.03–1.79), P = 0.03) and homozygote model (CC vs. TT: OR = 10.63, 95% CI (3.39–33.33), P < 0.00001) in the Caucasian subgroup were discovered, which is very novel and striking. Also novel discoveries were found in the allelic model (C vs. T: OR = 1.37, 95% CI (1.08–1.73), P = 0.009), dominant model (CC + CT vs. TT: OR = 1.48, 95% CI (1.08–2.02), P = 0.01) and homozygote model (CC vs. TT: OR = 2.84, 95% CI (1.36–5.92), P = 0.005) when we compared patients with mild influenza with healthy individuals. Our meta-analysis suggests that single-nucleotide T to C polymorphism of IFITM3 associated with increasingly risk of severe and mild influenza in both Asian and Caucasian populations.

Significant ethnic and socio-economic disparities exist in infectious diseases (IDs) rates in New Zealand, so accurate measures of these characteristics are required. This study compared methods of ascribing ethnicity and socio-economic status. Children in the Growing Up in New Zealand longitudinal cohort were ascribed to self-prioritised, total response and single-combined ethnic groups. Socio-economic status was measured using household income, and both census-derived and survey-derived deprivation indices. Rates of ID hospitalisation were compared using linked administrative data. Self-prioritised ethnicity was simplest to use. Total response accounted for mixed ethnicity and allowed overlap between groups. Single-combined ethnicity required aggregation of small groups to maintain power but offered greater detail. Regardless of the method used, Māori and Pacific children, and children in the most socio-economically deprived households had a greater risk of ID hospitalisation. Risk differences between self-prioritised and total response methods were not significant for Māori and Pacific children but single-combined ethnicity revealed a diversity of risk within these groups. Household income was affected by non-random missing data. The census-derived deprivation index offered a high level of completeness with some risk of multicollinearity and concerns regarding the ecological fallacy. The survey-derived index required extra questions but was acceptable to participants and provided individualised data. Based on these results, the use of single-combined ethnicity and an individualised survey-derived index of deprivation are recommended where sample size and data structure allow it.