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Scrub typhus is a life-threatening disease caused by Orientia tsutsugamushi, an obligate intracellular bacterium transmitted by the larvae of trombiculid mites (1). Only biting larvae of Asian scrub typhus chiggers [...]

Using detailed exposure information on COVID-19 cases, we estimated the mean latent period to be 5.5 (95% CI: 5.1–5.9) days, shorter than the mean incubation period (6.9 days). Laboratory testing may allow shorter quarantines since 95% of COVID-19 cases shed virus within 10.6 (95% CI: 9.6–11.6) days of infection. [ABSTRACT FROM AUTHOR]

Background: To understand Clonorchis sinensis reinfection and the determinants of reinfection in endemic areas is important in establishment of control measures.Methods: A prospective cohort study was implemented in Hengxian County, Guangxi, China. Individuals with C. sinensis infection were completely treated, and those cured were enrolled as study subjects and followed up for 3, 6, and 12 months. The reinfection frequency and incidence were calculated, and a multivariable Cox proportional hazard model was constructed to capture reinfection determinants.Results: Among 635 enrolled subjects, 436 (68.7%) completed follow-up. Of these, 177 (40.6%) were reinfected; 133 (75.1%) were reinfected once, 41 (23.2%) twice, and 3 (1.7%) three times. The incidence of reinfection was 64.0 per 100 person-years. Men (adjusted hazard ratio [aHR], 1.67; 95% confidence interval [CI], 1.14-2.44), those with underlying diseases (aHR, 1.41; 95% CI, 1.02-1.95), and those with moderate- or heavy-intensity infections (aHR, 1.45; 95% CI, 1.14-1.85) had increasing reinfection probabilities.Conclusions: C. sinensis reinfection is high in endemic areas. Men and high-intensity infection are important determinants of reinfection. Repeated chemotherapy is necessary to control reinfection and its associated morbidities, especially in high-risk individuals. In addition, behavioral education is advised to decrease overall reinfection in endemic areas. [ABSTRACT FROM AUTHOR]

Incubation period is an important parameter to inform quarantine period and to study transmission dynamics of infectious diseases. We conducted a systematic review and meta-analysis on published estimates of the incubation period distribution of coronavirus disease 2019, and showed that the pooled median of the point estimates of the mean, median and 95th percentile for incubation period are 6.3 days (range, 1.8–11.9 days), 5.4 days (range, 2.0–17.9 days), and 13.1 days (range, 3.2–17.8 days), respectively. Estimates of the mean and 95th percentile of the incubation period distribution were considerably shorter before the epidemic peak in China compared to after the peak, and variation was also noticed for different choices of methodological approach in estimation. Our findings implied that corrections may be needed before directly applying estimates of incubation period into control of or further studies on emerging infectious diseases. [ABSTRACT FROM AUTHOR]

Tracing and isolation of close contacts is used to control outbreaks of coronavirus disease 2019 (COVID‐19) in China. However, risk factors associated with the occurrence of COVID‐19 among close contacts have not been well described. A total of 106 household contacts were included in this study, of whom 19 developed into COVID‐19 cases, and the secondary attack rate was 17.9%. Multivariable analysis showed that increasing risk of occurrence of COVID‐19 among household contacts was associated with female index patients (adjusted hazard ratio [aHR] = 3.84, 95% CI = 1.07–13.78), critical disease index patients (aHR = 7.58, 95% CI = 1.66–34.66), effective contact duration with index patients > 2 days (aHR = 4.21, 95% CI = 1.29–13.73), and effective contact duration > 11 days (aHR = 17.88, 95% CI = 3.26–98.01). The sex and disease severity of index patients with COVID‐19 and longer effective contact duration with patients with confirmed COVID‐19 could help epidemiologists to identify potential COVID‐19 cases among household contacts at an early stage. [ABSTRACT FROM AUTHOR]

Scrub typhus, a miteborne rickettsiosis, has emerged in many areas globally. We analyzed the incidence and spatial-temporal distribution of scrub typhus in China during 1952-1989 and 2006-2016 using national disease surveillance data. A total of 133,623 cases and 174 deaths were recorded. The average annual incidence was 0.13 cases/100,000 population during 1952-1989; incidence increased sharply from 0.09/100,000 population in 2006 to 1.60/100,000 population in 2016. The disease, historically endemic to southern China, has expanded to all the provinces across both rural and urban areas. We identified 3 distinct seasonal patterns nationwide; infections peaked in summer in the southwest, summer-autumn in the southeast, and autumn in the middle-east. Persons >40 years of age and in nonfarming occupations had a higher risk for death. The changing epidemiology of scrub typhus in China warrants an enhanced disease control and prevention program. [ABSTRACT FROM AUTHOR]

Introduction: This study investigated the lagged correlation between Baidu Index for influenza-related keywords and influenza-like illness percentage (ILI%) across regions in China. The aim is to establish a scientific foundation for utilizing Baidu Index as an early warning tool for influenza-like illness epidemics., Methods: In this study, data on ILI% and Baidu Index were collected from 30 provincial-level administrative divisions (PLADs) spanning April 2014 to March 2019. The Baidu Index was categorized into Overall Index, Ordinary Index, Prevention Index, Symptom Index, and Treatment Index based on search query themes. The lagged correlation between the Baidu Index and ILI% was examined through the cross-correlation function (CCF) method., Results: Correlating the Baidu Overall Index of 30 PLADs with ILI% revealed CCF values ranging from 0.46 to 0.86, with a median lag of 0.5 days. Subcategory analysis indicated that the Prevention Index and Symptom Index exhibited quicker responses to ILI%, with median lags of -9 and -0.5 days, respectively, compared to 0 and 3 days for the Ordinary and Treatment Indexes. The median lag days between the Baidu Index and the ILI% were earlier in the northern PLADs compared to the southern PLADs., Discussion: The Prevention and Symptom Indexes show promising predictive capabilities for influenza-like illness epidemics., Competing Interests: No conflicts of interest., (Copyright and License information: Editorial Office of CCDCW, Chinese Center for Disease Control and Prevention 2024.)

Social distancing measures (SDMs) are community-level interventions that aim to reduce person-to-person contacts in the community. SDMs were a major part of the responses first to contain, then to mitigate, the spread of SARS-CoV-2 in the community. Common SDMs included limiting the size of gatherings, closing schools and/or workplaces, implementing work-from-home arrangements, or more stringent restrictions such as lockdowns. This systematic review summarized the evidence for the effectiveness of nine SDMs. Almost all of the studies included were observational in nature, which meant that there were intrinsic risks of bias that could have been avoided were conditions randomly assigned to study participants. There were no instances where only one form of SDM had been in place in a particular setting during the study period, making it challenging to estimate the separate effect of each intervention. The more stringent SDMs such as stay-at-home orders, restrictions on mass gatherings and closures were estimated to be most effective at reducing SARS-CoV-2 transmission. Most studies included in this review suggested that combinations of SDMs successfully slowed or even stopped SARS-CoV-2 transmission in the community. However, individual effects and optimal combinations of interventions, as well as the optimal timing for particular measures, require further investigation. This article is part of the theme issue 'The effectiveness of non-pharmaceutical interventions on the COVID-19 pandemic: the evidence'.

Background: Hong Kong followed a strict COVID-19 elimination strategy in 2020. We estimated the impact of the COVID-19 pandemic responses on all-cause and cause-specific hospitalizations and deaths in 2020., Methods: Interrupted time-series analysis using negative binomial regression accounting for seasonality and long-term trend was used on weekly 2010-2020 data to estimate the change in hospitalization risk and excess mortality occurring both within and out of hospitals., Findings: In 2020, as compared to a 2010-2019 baseline, we observed an overall reduction in all-cause hospitalizations, and a concurrent increase in deaths. The overall hospitalization reduction (per 100,000 population) was 4809 (95% CI: 4692, 4926) in 2020, with respiratory diseases (632, 95% CI: 607, 658) and cardiovascular diseases (275, 95% CI: 264, 286) contributing most. The overall excess mortality (per 100,000 population) was 25 (95% CI: 23, 27) in 2020, mostly among individuals with pre-existing cardiovascular diseases (12, 95% CI: 11, 13). A reduction in excess in-hospital mortality (-10 per 100,000, 95% CI: -12, -8) was accompanied by an increase in excess out-of-hospital mortality (32, 95% CI: 29, 34)., Interpretation: The COVID-19 pandemic might have caused indirect impact on population morbidity and mortality likely through changed healthcare seeking particularly in youngest and oldest individuals and those with cardiovascular diseases. Better healthcare planning is needed during public health emergencies with disruptions in healthcare services., Funding: Health and Medical Research Fund, Collaborative Research Fund, AIR@InnoHK and RGC Senior Research Fellow Scheme, Hong Kong., Competing Interests: BJC consults for AstraZeneca, Fosun Pharma, GlaxoSmithKline, Moderna, Pfizer, Roche and Sanofi Pasteur. JN previously worked for, and owns shares in, Sanofi. The other authors report no other potential conflicts of interest., (© 2022 The Authors.)

Competing Interests: Conflict of interest:No conflicts of interest were reported.