This study aimed to identify an appropriate simple mathematical model to fit the number of coronavirus disease 2019 (COVID-19) cases at the national level for the early portion of the pandemic, before significant public health interventions could be enacted. The total number of cases for the COVID-19 epidemic over time in 28 countries was analysed and fit to several simple rate models. The resulting model parameters were used to extrapolate projections for more recent data. While the Gompertz growth model (mean R2 = 0.998) best fit the current data, uncertainties in the eventual case limit introduced significant model errors. However, the quadratic rate model (mean R2 = 0.992) fit the current data best for 25 (89%) countries as determined by R2 values of the remaining models. Projection to the future using the simple quadratic model accurately forecast the number of future total number of cases 50% of the time up to 10 days in advance. Extrapolation to the future with the simple exponential model significantly overpredicted the total number of future cases. These results demonstrate that accurate future predictions of the case load in a given country can be made using this very simple model.

We used social network analysis (SNA) to study the novel coronavirus (COVID-19) outbreak in Karnataka, India, and to assess the potential of SNA as a tool for outbreak monitoring and control. We analysed contact tracing data of 1147 COVID-19 positive cases (mean age 34.91 years, 61.99% aged 11–40, 742 males), anonymised and made public by the Karnataka government. Software tools, Cytoscape and Gephi, were used to create SNA graphics and determine network attributes of nodes (cases) and edges (directed links from source to target patients). Outdegree was 1–47 for 199 (17.35%) nodes, and betweenness, 0.5–87 for 89 (7.76%) nodes. Men had higher mean outdegree and women, higher mean betweenness. Delhi was the exogenous source of 17.44% cases. Bangalore city had the highest caseload in the state (229, 20%), but comparatively low cluster formation. Thirty-four (2.96%) ‘super-spreaders’ (outdegree ⩾ 5) caused 60% of the transmissions. Real-time social network visualisation can allow healthcare administrators to flag evolving hotspots and pinpoint key actors in transmission. Prioritising these areas and individuals for rigorous containment could help minimise resource outlay and potentially achieve a significant reduction in COVID-19 transmission.

Better control of highly pathogenic avian influenza (HPAI) outbreaks requires deeper understanding of within-flock virus transmission dynamics. For such fatal diseases, daily mortality provides a proxy for disease incidence. We used the daily mortality data collected during the 2015 H5N2 HPAI outbreak in Minnesota turkey flocks to estimate the within-flock transmission rate parameter (β). The number of birds in Susceptible, Exposed, Infectious and Recovered compartments was inferred from the data and used in a generalised linear mixed model (GLMM) to estimate the parameters. Novel here was the correction of these data for normal mortality before use in the fitting process. We also used mortality threshold to determine HPAI-like mortality to improve the accuracy of estimates from the back-calculation approach. The estimated β was 3.2 (95% confidence interval (CI) 2.3–4.3) per day with a basic reproduction number of 12.8 (95% CI 9.2–17.2). Although flock-level estimates varied, the overall estimate was comparable to those from other studies. Sensitivity analyses demonstrated that the estimated β was highly sensitive to the bird-level latent period, emphasizing the need for its precise estimation. In all, for fatal poultry diseases, the back-calculation approach provides a computationally efficient means to obtain reasonable transmission parameter estimates from mortality data.

We compared antibiotic prescribing to older people in different settings to inform antibiotic stewardship interventions. We used data linkage to stratify individuals aged 65 years and over in Northern Ireland, 1st January 2012–31st December 2013, by residence: community dwelling, care home dwelling or ‘transitioned’ if admitted to a care home. The odds of being prescribed an antibiotic by residence were analysed using logistic regression, adjusting for patient demographics and selected medication use (proxy for co-morbidities). Trends in monthly antibiotic prescribing were examined in the 6 months pre- and post-admission to the care home. The odds of being prescribed at least one antibiotic were twofold higher in care homes compared with community dwellers (adjusted odds ratio 2.05, 95% CI 1.93–2.17). There was a proportionate increase of 51.5% in the percentage prescribed an antibiotic on admission, with a monthly average of 23% receiving an antibiotic in the 6 months post admission. While clinical need likely accounts for some of the observed antibiotic prescribing in care homes we cannot rule out more liberal prescribing, given the twofold difference between care home residents and their community dwelling peers having accounted for co-morbidities. The appropriateness of antibiotic prescribing in the care home setting should be examined.

Shiga-toxin producing Escherichia coli (STEC) is a pathogen that can cause bloody diarrhoea and severe complications. Cases occur sporadically but outbreaks are also common. Understanding the incubation period distribution and factors influencing it will help in the investigation of exposures and consequent disease control. We extracted individual patient data for STEC cases associated with outbreaks with a known source of exposure in England and Wales. The incubation period was derived and cases were described according to patient and outbreak characteristics. We tested for heterogeneity in reported incubation period between outbreaks and described the pattern of heterogeneity. We employed a multi-level regression model to examine the relationship between patient characteristics such as age, gender and reported symptoms; and outbreak characteristics such as mode of transmission with the incubation period. A total of 205 cases from 41 outbreaks were included in the study, of which 64 cases (31%) were from a single outbreak. The median incubation period was 4 days. Cases reporting bloody diarrhoea reported shorter incubation periods compared with cases without bloody diarrhoea, and likewise, cases aged between 40 and 59 years reported shorter incubation period compared with other age groups. It is recommended that public health officials consider the characteristics of cases involved in an outbreak in order to inform the outbreak investigation and the period of exposure to be investigated.

In Sierra Leone, the Ebola virus disease (EVD) outbreak occurred with substantial differences between districts with someone even not affected. To monitor the epidemic, a community event-based surveillance system was set up, collecting data into the Viral Haemorrhagic Fever (VHF) database. We analysed the VHF database of Tonkolili district to describe the epidemiology of the EVD outbreak during July 2014–June 2015 (data availability). Multivariable analysis was used to identify risk factors for EVD, fatal EVD and barriers to healthcare access, by comparing EVD-positive vs. EVD-negative cases. Key-performance indicators for EVD response were also measured. Overall, 454 EVD-positive cases were reported. At multivariable analysis, the odds of EVD was higher among those reporting contacts with an EVD-positive/suspected case (odds ratio (OR) 2.47; 95% confidence interval (CI) 2.44–2.50; P < 0.01) and those attending funeral (OR 1.02; 95% CI 1.01–1.04; P < 0.01). EVD cases from Kunike chiefdom had a lower odds of death (OR 0.22; 95% CI 0.08–0.44; P < 0.01) and were also more likely to be hospitalised (OR 2.34; 95% CI 1.23–4.57; P < 0.05). Only 25.1% of alerts were generated within 1 day from symptom onset. EVD preparedness and response plans for Tonkolili should include social-mobilisation activities targeting Ebola/knowledge-attitudes-practice during funeral attendance, to avoid contact with suspected cases and to increase awareness on EVD symptoms, in order to reduce delays between symptom onset to alert generation and consequently improve the outbreak-response promptness.

Typhoid fever is an illness caused by Salmonella enterica serotype Typhi. In developing regions, it affects an estimated 20 million people annually, causing 200 000 deaths. Although uncommon, cases occur in the USA each year, predominantly due to international travel. During February 2015, the Oklahoma State Department of Health (OSDH) detected an outbreak of typhoid fever among residents of northwestern Oklahoma. OSDH conducted case-patient interviews to identify the source and symptomatic contacts. Whole genome sequencing (WGS) was performed to characterise the genetic relatedness of isolates among the four outbreak-associated pulsed-field gel electrophoresis (PFGE) patterns. We identified 38 cases, 25 confirmed and 13 probable, in two states. WGS revealed a 0–10 single-nucleotide polymorphism variation between isolates. Although we were unable to determine the source, almost all case-patients were members of the Marshallese community that attended a common event in Oklahoma, or were contacts to a confirmed case. This is the largest outbreak of typhoid fever in the USA since 1989, and first to apply WGS to complement interpretation of PFGE results during a typhoid fever outbreak investigation. This investigation illustrates the potential risk of outbreaks among communities comprised of international populations from regions where typhoid fever remains endemic.

In September 2016, an imported case of measles in Edinburgh in a university student resulted in a further 17 confirmed cases during October and November 2016. All cases were genotype D8 and were associated with a virus strain most commonly seen in South East Asia. Twelve of the 18 cases were staff or students at a university in Edinburgh and 17 cases had incomplete or unknown measles mumps rubella (MMR) vaccination status. The public health response included mass follow-up of all identified contacts, widespread communications throughout universities in Edinburgh and prompt vaccination clinics at affected campuses. Imported cases of measles pose a significant risk to university student cohorts who may be undervaccinated, include a large number of international students and have a highly mobile population. Public health departments should work closely with universities to promote MMR uptake and put in place mass vaccination plans to prevent rapidly spreading measles outbreaks in higher educational settings in future.

Changes in seroprevalence of cysticercosis diagnosed in Chandigarh, India between 1998 and 2014 were investigated by extraction and analysis of data from records held at the Postgraduate Institute of Medical Education and Research in Chandigarh. Among the total number of samples for which cysticercosis had been suspected during this period (N = 9650), 1716 (17·8%) were seropositive. Adults were more likely to be seropositive than children, and women were more likely to be seropositive than men. In addition to there being fewer patients with suspicion of cysticercosis over the data analysis period, the proportion of patients seropositive also reduced significantly. Despite these reductions, which are probably associated with improved infrastructure and sanitation within Chandigarh, and despite meat consumption being relatively rare in this area, the extent of cysticercosis in this population remains problematic. Further efforts should be made to reduce transmission of this infection, with particular emphasis on women. Such efforts should follow the One Health concept, and involve medical efforts (including diagnosis and treatment of T. solium tapeworm carriers), veterinary efforts directed towards meat inspection and prevention of infection of pigs, and environmental health and sanitation engineers (to minimize environmental contamination with human waste).

South Africa's paediatric antiretroviral therapy (ART) programme is managed using a monitoring and evaluation tool known as TIER.Net. This electronic system has several advantages over paper-based systems, allowing profiling of the paediatric ART programme over time. We analysed anonymized TIER.Net data for HIV-infected children aged <15 years who had initiated ART in a rural district of South Africa between 2005 and 2014. We performed Kaplan–Meier survival analysis to assess outcomes over time. Records of 5461 children were available for analysis; 3593 (66%) children were retained in care. Losses from the programme were higher in children initiated on treatment in more recent years (P < 0·0001) and in children aged ≤1 year at treatment initiation (P < 0·0001). For children aged <3 years, abacavir was associated with a significantly higher rate of loss from the programme compared to stavudine (hazard ratio 1·9, P < 0·001). Viral load was suppressed in 48–52% of the cohort, with no significant change over the years (P = 0·398). Analysis of TIER.Net data over time provides enhanced insights into the performance of the paediatric ART programme and highlights interventions to improve programme performance.

We reviewed the first 21 years (1991–2011) of Australia's National Notifiable Diseases Surveillance System (NNDSS). All nationally notified diseases (except HIV/AIDS and Creutzfeldt–Jakob disease) were analysed by disease group (n = 8), jurisdiction (six states and two territories), Indigenous status, age group and notification year. In total, 2 421 134 cases were analysed. The 10 diseases with highest notification incidence (chlamydial infection, campylobacteriosis, varicella zoster, hepatitis C, influenza, pertussis, salmonellosis, hepatitis B, gonococcal infection, and Ross River virus infection) comprised 88% of all notifications. Annual notification incidence was 591 cases/100 000, highest in the Northern Territory (2598/100 000) and in children aged <5 years (698/100 000). A total of 8·4% of cases were Indigenous Australians. Notification incidence increased by 6·4% per year (12% for sexually transmissible infections and 15% for vaccine-preventable diseases). The number of notifiable diseases also increased from 37 to 65. The number and incidence of notifications increased throughout the study period, partly due to addition of diseases to the NNDSS and increasing availability of sensitive diagnostic tests. The most commonly notified diseases require a range of public health responses addressing high-risk sexual and drug-use behaviours, food safety and immunization. Our results highlight populations with higher notification incidence that might require tailored public health interventions.

Altered microbial communities are thought to play an important role in eosinophilic oesophagitis, an allergic inflammatory condition of the oesophagus. Identification of the majority of organisms present in human-associated microbial communities is feasible with the advent of high throughput sequencing technology. However, these data consist of non-negative, highly skewed sequence counts with a large proportion of zeros. In addition, hierarchical study designs are often performed with repeated measurements or multiple samples collected from the same subject, thus requiring approaches to account for within-subject variation, yet only a small number of microbiota studies have applied hierarchical regression models. In this paper, we describe and illustrate the use of a hierarchical regression-based approach to evaluate multiple factors for a small number of organisms individually. More specifically, the zero-inflated negative binomial mixed model with random effects in both the count and zero-inflated parts is applied to evaluate associations with disease state while adjusting for potential confounders for two organisms of interest from a study of human microbiota sequence data in oesophagitis.

A challenge to the development of foodborne illness prevention measures is determining the sources of enteric illness. Microbial subtyping source-attribution models attribute illnesses to various sources, requiring data characterizing bacterial isolate subtypes collected from human and food sources. We evaluated the use of antimicrobial resistance data on isolates of Salmonella enterica serotype Hadar, collected from ill humans, food animals, and from retail meats, in two microbial subtyping attribution models. We also compared model results when either antimicrobial resistance or pulsed-field gel electrophoresis (PFGE) patterns were used to subtype isolates. Depending on the subtyping model used, 68–96% of the human infections were attributed to meat and poultry food products. All models yielded similar outcomes, with 86% [95% confidence interval (CI) 80–91] to 91% (95% CI 88–96) of the attributable infections attributed to turkey, and 6% (95% CI 2–10) to 14% (95% CI 8–20) to chicken. Few illnesses (<3%) were attributed to cattle or swine. Results were similar whether the isolates were obtained from food animals during processing or from retail meat products. Our results support the view that microbial subtyping models are a flexible and robust approach for attributing Salmonella Hadar.

Suboptimal uptake of the measles, mumps and rubella (MMR) vaccine by certain socioeconomic groups may have contributed to recent large measles outbreaks in the UK. We investigated whether socioeconomic deprivation was associated with MMR vaccine uptake over 16 years. Using immunization data for 72 351 children born between 1995 and 2012 in Liverpool, UK, we examined trends in vaccination uptake. Generalized linear models were constructed to examine the relative effect of socioeconomic deprivation and year of birth on MMR uptake. Uptake of MMR1 by age 24 months ranged between 82·5% in 2003 [95% confidence interval (CI) 81·2–83·7] and 93·4% in 2012 (95% CI 92·7–94·2). Uptake of MMR2 by age 60 months ranged between 65·3% (95% CI 64·4–67·4) in 2006 and 90·3% (95% CI 89·4–91·2) in 2012. In analysis adjusted for year of birth and sex, children in the most deprived communities were at significantly greater risk of not receiving MMR1 [risk ratio (RR) 1·70, 95% CI 1·45–1·99] and MMR2 (RR 1·36, 95% CI 1·22–1·52). Higher unemployment and lower household income were significantly associated with low uptake. Contrary to concerns about lower MMR uptake in affluent families, over 16 years, children from the most socioeconomically deprived communities have consistently had the lowest MMR uptake. Targeted catch-up campaigns and strategies to improve routine immunization uptake in deprived areas are needed to minimize the risk of future measles outbreaks.

Syndromic surveillance (SyS) systems currently exploit various sources of health-related data, most of which are collected for purposes other than surveillance (e.g. economic). Several European SyS systems use data collected during meat inspection for syndromic surveillance of animal health, as some diseases may be more easily detected post-mortem than at their point of origin or during the ante-mortem inspection upon arrival at the slaughterhouse. In this paper we use simulation to evaluate the performance of a quasi-Poisson regression (also known as an improved Farrington) algorithm for the detection of disease outbreaks during post-mortem inspection of slaughtered animals. When parameterizing the algorithm based on the retrospective analyses of 6 years of historic data, the probability of detection was satisfactory for large (range 83–445 cases) outbreaks but poor for small (range 20–177 cases) outbreaks. Varying the amount of historical data used to fit the algorithm can help increasing the probability of detection for small outbreaks. However, while the use of a 0·975 quantile generated a low false-positive rate, in most cases, more than 50% of outbreak cases had already occurred at the time of detection. High variance observed in the whole carcass condemnations time-series, and lack of flexibility in terms of the temporal distribution of simulated outbreaks resulting from low reporting frequency (monthly), constitute major challenges for early detection of outbreaks in the livestock population based on meat inspection data. Reporting frequency should be increased in the future to improve timeliness of the SyS system while increased sensitivity may be achieved by integrating meat inspection data into a multivariate system simultaneously evaluating multiple sources of data on livestock health.

The US Center for Medicare and Medicaid Services (CMS) requires nursing homes and long-term-care facilities to document residents' vaccination status on the Resident Assessment Instrument (RAI). Vaccinating residents can prevent costly hospital admissions and deaths. CMS and public health officials use RAI data to measure vaccination rates in long-term-care residents and assess the quality of care in nursing homes. We assessed the accuracy of RAI data against medical records in 39 nursing homes in Florida, Georgia, and Wisconsin. We randomly sampled residents in each home during the 2010–2011 and 2011–2012 influenza seasons. We collected data on receipt of influenza vaccination from charts and RAI data. Our final sample included 840 medical charts with matched RAI records. The agreement rate was 0·86. Using the chart as a gold standard, the sensitivity of the RAI with respect to influenza vaccination was 85% and the specificity was 77%. Agreement rates varied within facilities from 55% to 100%. Monitoring vaccination rates in the population is important for gauging the impact of programmes and policies to promote adherence to vaccination recommendations. Use of data from RAIs is a reasonable approach for gauging influenza vaccination rates in nursing-home residents.

Shiga toxin-producing Escherichia coli (STEC) infections have been associated with severe illness. Ruminants are seen as the main reservoir and the major transmission route is considered to be foodborne. In The Netherlands, a case-control study was conducted, using data collected during 2008–2012. Patients were interviewed and controls completed a self-administered questionnaire. Patients travelling abroad were excluded from the analyses. STEC O157 and non-O157 were examined separately and differentiated into two age groups (<10 years, ⩾10 years). We included 130 O157 cases, 78 non-O157 cases and 1563 controls. In both age groups of O157 patients, raw spreadable sausage was the main risk factor for infection. For STEC non-O157 cases aged <10 years, contact with farm animals was the main risk factor and in non-O157 cases aged ⩾10 years, consumption of beef was the main risk factor. During 2008–2012, risk factors for STEC infections in the Dutch population differed between age groups and serogroup categories, and were related to eating meat and contact with farm animals. Advising the public about the risks of consuming raw or undercooked meat (products) and hygiene habits in case of contact with farm animals, could help in the prevention of STEC infections.

The objective of this study was to measure the association between deprivation and incidence of 21 infectious diseases in the North East of England (2007–2011). We used count regression models with the Index of Multiple Deprivation and population/landscape data for small areas (~1500 persons). Deprivation significantly predicted incidence (P < 0·05) for 17 infectious diseases. The direction of association was broadly consistent within groups: increased incidence with increased deprivation for all three bloodborne viruses, 2/3 invasive bacterial diseases, 4/5 sexually transmitted infections (STI) and tuberculosis (TB); decreased incidence with increased deprivation for 5/6 infectious intestinal diseases (IID) and 2/3 vaccine-preventable diseases. Associations were removed for all but one IID (E. coli O157 infection) after accounting for recent foreign travel. Hepatitis C virus, TB and STI are priority infections for reduction of inequalities associated with deprivation in the North East of England.

This study evaluates the usefulness of spatio-temporal statistical tools to detect outbreaks using routine surveillance data where limited epidemiological information is available. A dataset from 2002 to 2007 containing information regarding date, origin, source and serotype of 29 586 Salmonella isolates from Thailand was analysed. Data was grouped into human and non-human categories and the analysis was performed for the top five occurring serovars for each year of the study period. A total 91 human and 39 non-human significant spatio-temporal clusters were observed, accounting for 11% and 16% of the isolates, respectively. Serovar-specific associations between human and non-human clusters were also evaluated. Results show that these statistical tools can provide information for use in outbreak prevention and detection, in countries where only limited data is available. Moreover, it is suggested that monitoring non-human reservoirs can be relevant in predicting future Salmonella human cases.

We examined the spatial distribution pattern and meteorological drivers of dengue fever (DF) in Guangdong Province, China. Annual incidence of DF was calculated for each county between 2005 and 2011 and the geographical distribution pattern of DF was examined using Moran's I statistic and excess risk maps. A time-stratified case-crossover study was used to investigate the short-term relationship between DF and meteorological factors and the Southern Oscillation Index (SOI). High-epidemic DF areas were restricted to the Pearl River Delta region and the Han River Delta region, Moran's I of DF distribution was significant from 2005 to 2006 and from 2009 to 2011. Daily vapour pressure, mean and minimum temperatures were associated with increased DF risk. Maximum temperature and SOI were negatively associated with DF transmission. The risk of DF was non-randomly distributed in the counties in Guangdong Province. Meteorological factors could be important predictors of DF transmission.