Typical enteropathogenic Escherichia coli (tEPEC) infection is a major cause of diarrhoea and contributor to mortality in children <5 years old in developing countries. Data were analysed from the Global Enteric Multicenter Study examining children <5 years old seeking care for moderate-to-severe diarrhoea (MSD) in Kenya. Stool specimens were tested for enteric pathogens, including by multiplex polymerase chain reaction for gene targets of tEPEC. Demographic, clinical and anthropometric data were collected at enrolment and ~60-days later; multivariable logistic regressions were constructed. Of 1778 MSD cases enrolled from 2008 to 2012, 135 (7.6%) children tested positive for tEPEC. In a case-to-case comparison among MSD cases, tEPEC was independently associated with presentation at enrolment with a loss of skin turgor (adjusted odds ratio (aOR) 2.08, 95% confidence interval (CI) 1.37–3.17), and convulsions (aOR 2.83, 95% CI 1.12–7.14). At follow-up, infants with tEPEC compared to those without were associated with being underweight (OR 2.2, 95% CI 1.3–3.6) and wasted (OR 2.5, 95% CI 1.3–4.6). Among MSD cases, tEPEC was associated with mortality (aOR 2.85, 95% CI 1.47–5.55). This study suggests that tEPEC contributes to morbidity and mortality in children. Interventions aimed at defining and reducing the burden of tEPEC and its sequelae should be urgently investigated, prioritised and implemented.

One of the largest nationwide bursts of the first COVID-19 outbreak occurred in Spain, where infection expanded in densely populated areas through March 2020. We analyse the cumulative growth curves of reported cases and deaths in all Spain and two highly populated regions, Madrid and Catalonia, identifying changes and sudden shifts in their exponential growth rate through segmented Poisson regressions. We associate these breakpoints with a timeline of key events and containment measures, and data on policy stringency and citizen mobility. Results were largely consistent for infections and deaths in all territories, showing four major shifts involving 19–71% reductions in growth rates originating from infections before 3 March and on 5–8, 10–12 and 14–18 March, but no identifiable effect of the strengthened lockdown of 29–30 March. Changes in stringency and mobility were only associated to the latter two shifts, evidencing an early deceleration in COVID-19 spread associated to personal hygiene and social distancing recommendations, followed by a stronger decrease when lockdown was enforced, leading to the contention of the outbreak by mid-April. This highlights the importance of combining public health communication strategies and hard confinement measures to contain epidemics.

Pertussis is a highly contagious infectious disease and remains an important cause of mortality and morbidity worldwide. Over the last decade, vaccination has greatly reduced the burden of pertussis. Yet, uncertainty in individual vaccination coverage and ineffective case surveillance systems make it difficult to estimate burden and the related quantity of population-level susceptibility, which determines population risk. These issues are more pronounced in low-income settings where coverage is often overestimated, and case numbers are under-reported. Serological data provide a direct characterisation of the landscape of susceptibility to infection; and can be combined with vaccination coverage and basic theory to estimate rates of exposure to natural infection. Here, we analysed cross-sectional data on seropositivity against pertussis to identify spatial and age patterns of susceptibility in children in Madagascar. A large proportion of individuals surveyed were seronegative; however, there were patterns suggestive of natural infection in all the regions analysed. Improvements in vaccination coverage are needed to help prevent additional burden of pertussis in the country.

Google's ‘Community Mobility Reports’ (CMR) detail changes in activity and mobility occurring in response to COVID-19. They thus offer the unique opportunity to examine the relationship between mobility and disease incidence. The objective was to examine whether an association between COVID-19-confirmed case numbers and levels of mobility was apparent, and if so then to examine whether such data enhance disease modelling and prediction. CMR data for countries worldwide were cross-correlated with corresponding COVID-19-confirmed case numbers. Models were fitted to explain case numbers of each country's epidemic. Models using numerical date, contemporaneous and distributed lag CMR data were contrasted using Bayesian Information Criteria. Noticeable were negative correlations between CMR data and case incidence for prominent industrialised countries of Western Europe and the North Americas. Continent-wide examination found a negative correlation for all continents with the exception of South America. When modelling, CMR-expanded models proved superior to the model without CMR. The predictions made with the distributed lag model significantly outperformed all other models. The observed relationship between CMR data and case incidence, and its ability to enhance model quality and prediction suggests data related to community mobility could prove of use in future COVID-19 modelling.

Understanding risk factors for death from Covid-19 is key to providing good quality clinical care. We assessed the presenting characteristics of the ‘first wave’ of patients with Covid-19 at Royal Oldham Hospital, UK and undertook logistic regression modelling to investigate factors associated with death. Of 470 patients admitted, 169 (36%) died. The median age was 71 years (interquartile range 57–82), and 255 (54.3%) were men. The most common comorbidities were hypertension (n = 218, 46.4%), diabetes (n = 143, 30.4%) and chronic neurological disease (n = 123, 26.1%). The most frequent complications were acute kidney injury (AKI) (n = 157, 33.4%) and myocardial injury (n = 21, 4.5%). Forty-three (9.1%) patients required intubation and ventilation, and 39 (8.3%) received non-invasive ventilation. Independent risk factors for death were increasing age (odds ratio (OR) per 10 year increase above 40 years 1.87, 95% confidence interval (CI) 1.57–2.27), hypertension (OR 1.72, 95% CI 1.10–2.70), cancer (OR 2.20, 95% CI 1.27–3.81), platelets <150 × 103/μl (OR 1.93, 95% CI 1.13–3.30), C-reactive protein ≥100 μg/ml (OR 1.68, 95% CI 1.05–2.68), >50% chest radiograph infiltrates (OR 2.09, 95% CI 1.16–3.77) and AKI (OR 2.60, 95% CI 1.64–4.13). There was no independent association between death and gender, ethnicity, deprivation level, fever, SpO2/FiO2, lymphopoenia or other comorbidities. These findings will inform clinical and shared decision making, including use of respiratory support and therapeutic agents.

Most of the existing prediction models for COVID-19 lack validation, are inadequately reported or are at high risk of bias, a reason which has led to discourage their use. Few existing models have the potential to be extensively used by healthcare providers in low-resource settings since many require laboratory and imaging predictors. Therefore, we sought to develop and validate a multivariable prediction model of death in Mexican patients with COVID-19, by using demographic and patient history predictors. We conducted a national retrospective cohort study in two different sets of patients from the Mexican COVID-19 Epidemiologic Surveillance Study. Patients with a positive reverse transcription-polymerase chain reaction for SARS-CoV-2 and complete unduplicated data were eligible. In total, 83 779 patients were included to develop the scoring system through a multivariable Cox regression model; 100 000, to validate the model. Eight predictors (age, sex, diabetes, chronic obstructive pulmonary disease, immunosuppression, hypertension, obesity and chronic kidney disease) were included in the scoring system called PH-Covid19 (range of values: −2 to 25 points). The predictive model has a discrimination of death of 0.8 (95% confidence interval (CI) 0.796–0.804). The PH-Covid19 scoring system was developed and validated in Mexican patients to aid clinicians to stratify patients with COVID-19 at risk of fatal outcomes, allowing for better and efficient use of resources.

Children are important transmitters of infection. Within schools they encounter large numbers of contacts and infections can spread easily causing outbreaks. However, not all schools are affected equally. We conducted a retrospective analysis of school outbreaks to identify factors associated with the risk of gastroenteritis, influenza, rash or other outbreaks. Data on reported school outbreaks in England were obtained from Public Health England and linked with data from the Department for Education and the Office for Standards in Education, Children's Services and Skills (Ofsted). Primary and all-through schools were found to be at increased risk of outbreaks, compared with secondary schools (odds ratio (OR) 5.82, 95% confidence interval (CI) 4.50–7.58 and OR 4.66, 95% CI 3.27–6.61, respectively). School size was also significantly associated with the risk of outbreaks, with higher odds associated with larger schools. Attack rates were higher in gastroenteritis and influenza outbreaks, with lower attack rates associated with rashes (relative risk 0.17, 95% CI 0.15–0.20). Deprivation and Ofsted rating were not associated with either outbreak occurrence or the subsequent attack rate. This study identifies primary and all-through schools as key settings for health protection interventions. Public health teams need to work closely with these schools to encourage early identification and reporting of outbreaks.

This study aimed to analyse the spatial–temporal distribution of COVID-19 mortality in Sergipe, Northeast, Brazil. It was an ecological study utilising spatiotemporal analysis techniques that included all deaths confirmed by COVID-19 in Sergipe, from 2 April to 14 June 2020. Mortality rates were calculated per 100 000 inhabitants and the temporal trends were analysed using a segmented log-linear model. For spatial analysis, the Kernel estimator was used and the crude mortality rates were smoothed by the empirical Bayesian method. The space–time prospective scan statistics applied the Poisson's probability distribution model. There were 391 COVID-19 registered deaths, with the majority among ⩾60 years old (62%) and males (53%). The most prevalent comorbidities were hypertension (40%), diabetes (31%) and cardiovascular disease (15%). An increasing mortality trend across the state was observed, with a higher increase in the countryside. An active spatiotemporal cluster of mortality comprising the metropolitan area and neighbouring cities was identified. The trend of COVID-19 mortality in Sergipe was increasing and the spatial distribution of deaths was heterogeneous with progression towards the countryside. Therefore, the use of spatial analysis techniques may contribute to surveillance and control of COVID-19 pandemic.

An acute gastroenteritis (AGE) outbreak caused by a norovirus occurred at a hospital in Shanghai, China, was studied for molecular epidemiology, host susceptibility and serological roles. Rectal and environmental swabs, paired serum samples and saliva specimens were collected. Pathogens were detected by real-time polymerase chain reaction and DNA sequencing. Histo-blood group antigens (HBGA) phenotypes of saliva samples and their binding to norovirus protruding proteins were determined by enzyme-linked immunosorbent assay. The HBGA-binding interfaces and the surrounding region were analysed by the MegAlign program of DNAstar 7.1. Twenty-seven individuals in two care units were attacked with AGE at attack rates of 9.02 and 11.68%. Eighteen (78.2%) symptomatic and five (38.4%) asymptomatic individuals were GII.6/b norovirus positive. Saliva-based HBGA phenotyping showed that all symptomatic and asymptomatic cases belonged to A, B, AB or O secretors. Only four (16.7%) out of the 24 tested serum samples showed low blockade activity against HBGA-norovirus binding at the acute phase, whereas 11 (45.8%) samples at the convalescence stage showed seroconversion of such blockade. Specific blockade antibody in the population played an essential role in this norovirus epidemic. A wide HBGA-binding spectrum of GII.6 supports a need for continuous health attention and surveillance in different settings.

Drug-induced liver injury (DILI) is a common adverse drug reaction leading to the interruption of tuberculosis (TB) therapy. We aimed to identify whether the hepatitis B virus (HBV) infection would increase the risk of DILI during first-line TB treatment. A meta-analysis of cohort studies searched in PubMed, Web of Science and China National Knowledge Infrastructure was conducted. Effect sizes were reported as risk ratios (RRs) and 95% confidence intervals (CIs) and calculated by R software. Sixteen studies with 3960 TB patients were eligible for analysis. The risk of DILI appeared to be higher in TB patients co-infected with HBV (RR 2.66; 95% CI 2.13–3.32) than those without HBV infection. Moreover, patients with positive hepatitis B e antigen (HBeAg) were more likely to develop DILI (RR 3.42; 95% CI 1.95–5.98) compared to those with negative HBeAg (RR 2.30; 95% CI 1.66–3.18). Co-infection with HBV was not associated with a higher rate of anti-TB DILI in latent TB patients (RR 4.48; 95% CI 0.80–24.99). The effect of HBV infection on aggravating anti-TB DILI was independent of study participants, whether they were newly diagnosed with TB or not. Besides, TB and HBV co-infection patients had a longer duration of recovery from DILI compared to non-co-infected patients (SMD 2.26; 95% CI 1.87–2.66). To conclude, the results demonstrate that HBV infection would increase the risk of DILI during TB therapy, especially in patients with positive HBeAg, and close liver function monitoring is needed for TB and HBV co-infection patients.

This study aimed at estimating the transmissibility of hepatitis C. The data for hepatitis C cases were collected in six districts in Xiamen City, China from 2004 to 2018. A population-mixed susceptible-infectious-chronic-recovered (SICR) model was used to fit the data and the parameters of the model were calculated. The basic reproduction number (R0) and the number of newly transmitted cases by a primary case per month (MNI) were adopted to quantitatively assess the transmissibility of hepatitis C virus (HCV). Eleven curve estimation models were employed to predict the trends of R0 and MNI in the city. The SICR model fits the reported HCV data well (P < 0.01). The median R0 of each district in Xiamen is 0.4059. R0 follows the cubic model curve, the compound curve and the power function curve. The median MNI of each district in Xiamen is 0.0020. MNI follows the cubic model curve, the compound curve and the power function curve. The transmissibility of HCV follows a decreasing trend, which reveals that under the current policy for prevention and control, there would be a high feasibility to eliminate the transmission of HCV in the city.

Despite high exposure to Middle East respiratory syndrome coronavirus (MERS-CoV), the predictors for seropositivity in the context of husbandry practices for camels in Eastern Africa are not well understood. We conducted a cross-sectional survey to describe the camel herd profile and determine the factors associated with MERS-CoV seropositivity in Northern Kenya. We enrolled 29 camel-owning households and administered questionnaires to collect herd and household data. Serum samples collected from 493 randomly selected camels were tested for anti-MERS-CoV antibodies using a microneutralisation assay, and regression analysis used to correlate herd and household characteristics with camel seropositivity. Households reared camels (median = 23 camels and IQR 16–56), and at least one other livestock species in two distinct herds; a home herd kept near homesteads, and a range/fora herd that resided far from the homestead. The overall MERS-CoV IgG seropositivity was 76.3%, with no statistically significant difference between home and fora herds. Significant predictors for seropositivity (P ⩽ 0.05) included camels 6–10 years old (aOR 2.3, 95% CI 1.0–5.2), herds with ⩾25 camels (aOR 2.0, 95% CI 1.2–3.4) and camels from Gabra community (aOR 2.3, 95% CI 1.2–4.2). These results suggest high levels of virus transmission among camels, with potential for human infection.

The epidemic of coronavirus disease 2019 (COVID-19) began in China and had spread rapidly to many other countries. This study aimed to identify risk factors associated with delayed negative conversion of SARS-CoV-2 in COVID-19 patients. In this retrospective single-centre study, we included 169 consecutive patients with confirmed COVID-19 in Zhongnan Hospital of Wuhan University from 15th January to 2nd March. The cases were divided into two groups according to the median time of SARS-CoV-2 negative conversion. The differences between groups were compared. In total, 169 patients had a median virus negative conversion time of 18 days (interquartile range: 11–25) from symptom onset. Compared with the patients with short-term negative conversion, those with long-term conversion had an older age, higher incidence of comorbidities, chief complaints of cough and chest distress/breath shortness and severer illness on admission, higher level of leucocytes, neutrophils, aspartate aminotransferase, creatine kinase and erythrocyte sedimentation rate (ESR), lower level of CD3+CD4+ lymphocytes and albumin and more likely to receive mechanical ventilation. In multivariate analysis, cough, leucocytes, neutrophils and ESR were positively correlated with delayed virus negative conversion, and CD3+CD4+ lymphocytes were negatively correlated. The integrated indicator of leucocytes, neutrophils and CD3+CD4+ lymphocytes showed a good performance in predicting the negative conversion within 2 weeks (area under ROC curve (AUC) = 0.815), 3 weeks (AUC = 0.804), 4 weeks (AUC = 0.812) and 5 weeks (AUC = 0.786). In conclusion, longer quarantine periods might be more justified for COVID-19 patients with cough, higher levels of leucocytes, neutrophils and ESR and lower levels of CD3+CD4+ lymphocytes.

Some studies have suggested that the Toll-like receptor 9 polymorphism (TLR9 rs352140) is closely related to the risk of bacterial meningitis (BM), but this is subject to controversy. This study set out to estimate whether the TLR9 rs352140 polymorphism confers an increased risk of BM. Relevant literature databases were searched including PubMed, Embase, the Cochrane Library and China National Knowledge Infrastructure (CNKI) up to August 2020. Seven case-control studies from four publications were enrolled in the present meta-analysis. Odds ratios (OR) and confidence intervals (95% CI) were calculated to estimate associations between BM risk and the target polymorphism. Significant associations identified were allele contrast (A vs. G: OR 0.66, 95% CI 0.59–0.75, P = 0.000), homozygote comparison (AA vs. AG/GG: OR 0.62, 95% CI 0.49–0.78, P = 0.000), heterozygote comparison (A vs. G: OR 0.74, 95% CI 0.61–0.91, P = 0.005), recessive genetic model (AA vs. AG/GG: OR 0.78, 95% CI 0.65–0.93, P = 0.006) and dominant genetic model (AA vs. AG/GG: OR 0.70, 95% CI 0.57–0.85, P = 0.000). The findings indicate that, in contrast to some studies, the TLR9 rs352140 polymorphism is associated with a decreased risk for BM.

Two hundred days after the first confirmed case of COVID-19 in Brazil, the epidemic has rapidly spread in metropolitan areas and advanced throughout the countryside. We followed the temporal epidemic pattern at São Paulo State, the most populous of the country, the first to have a confirmed case of COVID-19, and the one with the most significant number of cases until now. We analysed the number of new cases per day in each regional health department and calculated the effective reproduction number (Rt) over time. Social distance measures, along with improvement in testing and isolating positive cases, general population mask-wearing and standard health security protocols for essential and non-essential activities, were adopted and impacted on slowing down epidemic velocity but were insufficient to stop transmission.

Influenza is a major human respiratory pathogen. Due to the high levels of influenza-like illness (ILI) in Zhejiang, China, the control and prevention of influenza was challenging during the 2017–2018 season. To identify the clinical spectrum of illness related to influenza and characterise the circulating influenza virus strains during this period, the characteristics of ILI were studied. Viral sequencing and phylogenetic analyses were conducted to investigate the virus types, substitutions at the amino acid level and phylogenetic relationships between sequences. This study has shown that the 2017/18 influenza season was characterised by the co-circulation of influenza A (H1N1) pdm09, A (H3N2) and B viruses (both Yamagata and Victoria lineage). From week 36 of 2017 to week 12 of 2018, ILI cases accounted for 5.58% of the total number of outpatient and emergency patient visits at the surveillance sites. Several amino acid substitutions were detected. Vaccination mismatch may be a potential reason for the high percentage of ILI. Furthermore, it is likely that multiple viral introductions played a role in the endemic co-circulation of influenza in Zhejiang, China. More detailed information regarding the molecular epidemiology of influenza should be included in long-term influenza surveillance.

Using a stochastic model, we assess the risk of importation-induced local transmission chains in locations seeing few or no local transmissions and evaluate the role of quarantine in the mitigation of this risk. We find that the rate of importations plays a critical role in determining the risk that case importations lead to local transmission chains, more so than local transmission characteristics, i.e. strength of social distancing measures (NPI). The latter influences the severity of the outbreaks when they do take place. Quarantine after arrival in a location is an efficacious way to reduce the rate of importations. Locations that see no or low-level local transmission should ensure that the rate of importations remains low. A high level of compliance with post-arrival quarantine followed by testing achieves this objective with less of an impact than travel restrictions or bans.

Influenza vaccine effectiveness (VE) wanes over the course of a temperate climate winter season but little data are available from tropical countries with year-round influenza virus activity. In Singapore, a retrospective cohort study of adults vaccinated from 2013 to 2017 was conducted. Influenza vaccine failure was defined as hospital admission with polymerase chain reaction-confirmed influenza infection 2–49 weeks after vaccination. Relative VE was calculated by splitting the follow-up period into 8-week episodes (Lexis expansion) and the odds of influenza infection in the first 8-week period after vaccination (weeks 2–9) compared with subsequent 8-week periods using multivariable logistic regression adjusting for patient factors and influenza virus activity. Records of 19 298 influenza vaccinations were analysed with 617 (3.2%) influenza infections. Relative VE was stable for the first 26 weeks post-vaccination, but then declined for all three influenza types/subtypes to 69% at weeks 42–49 (95% confidence interval (CI) 52–92%, P = 0.011). VE declined fastest in older adults, in individuals with chronic pulmonary disease and in those who had been previously vaccinated within the last 2 years. Vaccine failure was significantly associated with a change in recommended vaccine strains between vaccination and observation period (adjusted odds ratio 1.26, 95% CI 1.06–1.50, P = 0.010).

The prevalence of asymptomatic infection by coronavirus disease 2019 (COVID-19) as a critical measure for effectiveness of mitigation strategy has been reported to be widely varied. In this study, we aimed to determine the prevalence of asymptomatic infection using serosurvey on general population. In a cross-sectional seroprevalence survey in Guilan province, Iran, the specific antibody against COVID-19 in a representative sample was detected using rapid test kits. Among 117 seropositive subjects, prevalence of asymptomatic infection was determined based on the history of symptoms during the preceding 3 months. The design-adjusted prevalence of asymptomatic infection was 57.2% (95% confidence interval (CI) 44–69). The prevalence was significantly lower in subjects with previous contacts to COVID-19 patients (12%, 95% CI 2–49) than others without (69%, 95% CI, 46–86). The lowest prevalence was for painful body symptom (74.4%). This study revealed that more than half of the infected COVID-19 patients had no symptoms. The implications of our findings include the importance of adopting public health measures such as social distancing and inefficiency of contact tracing to interrupt epidemic transmission.