Project description:During the early phase of the coronavirus disease epidemic in Hong Kong, 1,715 survey respondents reported high levels of perceived risk, mild anxiety, and adoption of personal-hygiene, travel-avoidance, and social-distancing measures. Widely adopted individual precautionary measures, coupled with early government actions, might slow transmission early in the outbreak.
Project description:Wastewater based epidemiology is a potential early warning tool for the detection of COVID-19 outbreak. Sewage surveillance for SARS-CoV-2 RNA was introduced in Hungary after the successful containment of the first wave of the pandemic to forecast the resurge of infections. Three wastewater treatment plants servicing the entire population (1.8 million) of the capital, Budapest were sampled weekly. 24 h composite (n = 44) and grab samples (n = 21) were concentrated by an in-house flat sheet membrane ultrafiltration method. The efficiency and reproducibility of the method was comparable to those previously published. SARS-CoV-2 RNA was quantified using RT-qPCR of the N gene. The first positive signal in sewage was detected 2 weeks before the rise in case numbers. Viral concentration and volume-adjusted viral load correlated to the weekly new cases from the same week and the rolling 7-day average of active cases in the subsequent week. The correlation was more pronounced in the ascending phase of the outbreak, data was divergent once case numbers plateaued. Wastewater surveillance was found to be effective in predicting the second wave of the outbreak in Hungary. Data indicated that even relatively low frequency (weekly) sampling is useful and at the same time, cost effective tool in outbreak detection.
Project description:Early warning signals (EWSs) aim to predict changes in complex systems from phenomenological signals in time series data. These signals have recently been shown to precede the emergence of disease outbreaks, offering hope that policymakers can make predictive rather than reactive management decisions. Here, using a novel, sequential analysis in combination with daily COVID-19 case data across 24 countries, we suggest that composite EWSs consisting of variance, autocorrelation and skewness can predict nonlinear case increases, but that the predictive ability of these tools varies between waves based upon the degree of critical slowing down present. Our work suggests that in highly monitored disease time series such as COVID-19, EWSs offer the opportunity for policymakers to improve the accuracy of urgent intervention decisions but best characterize hypothesized critical transitions.
Project description:Background: Studies showed that healthcare workers (HCWs) and pregnant women bore the burden of mental problems during the coronavirus disease 2019 (COVID-19) pandemic. While, few studies have focused on the psychological impact of COVID-19 pandemic on pregnant women who work at healthcare settings. This study aimed to investigate and compare the prevalence difference of psychological symptoms between pregnant HCWs and pregnant non-HCWs during the early stage of COVID-19 pandemic in China. Methods: A cross-sectional online survey with anonymous structured questionnaires was conducted from February 15 to March 9, 2020. A total of 205 pregnant women in Chongqing, China were recruited. The mental health status was assessed using symptom checklist-90 (SCL-90). Results: Our sample was composed of 83 pregnant HCWs (mean age = 29.8) and 122 pregnant non-HCWs (mean age = 30.8). The results suggested the prevalence of psychological symptoms (the factor score ≥2) among all pregnant women ranged from 6.83% (psychosis symptoms) to 17.56% (obsessive-compulsive symptoms). Compared with pregnant non-HCWs, pregnant HCWs reported higher prevalence of psychological symptoms in 10 factors of SCL-90. After controlling the confounding variables, multiple logistic regression demonstrated that pregnant HCWs experienced higher prevalence of psychological symptoms of somatization (18.07 vs. 5.74%, p = 0.006, aOR = 4.52), anxiety disorders (16.87 vs. 6.56%, p = 0.016, aOR = 3.54), and hostility (24.10 vs. 10.66%, p = 0.027, aOR = 2.70) than those among pregnant non-HCW. Conclusion: Our study indicated that pregnant HCWs were more likely to suffer from mental health distress than pregnant non-HCWs during the early stage of COVID-19 pandemic. It is vital to implement targeted psychological interventions for pregnant women, especially for pregnant HCWs to cope with distress when facing the emerging infectious diseases.
Project description:Human to human transmissible infectious diseases spread in a population using human interactions as its transmission vector. The early stages of such an outbreak can be modeled by a graph whose edges encode these interactions between individuals, the vertices. This article attempts to account for the case when each individual entails in different kinds of interactions which have therefore different probabilities of transmitting the disease. The majority of these results can be also stated in the language of percolation theory. The main contributions of the article are: (1) Extend to this setting some results which were previously known in the case when each individual has only one kind of interactions. (2) Find an explicit formula for the basic reproduction number [Formula: see text] which depends only on the probabilities of transmitting the disease along the different edges and the first two moments of the degree distributions of the associated graphs. (3) Motivated by the recent Covid-19 pandemic, we use the framework developed to compute the [Formula: see text] of a model disease spreading in populations whose trees and degree distributions are adjusted to several different countries. In this setting, we shall also compute the probability that the outbreak will not lead to an epidemic. In all cases we find such probability to be very low if no interventions are put in place.
Project description:In Singapore, after a major outbreak of dengue in 2005, another outbreak occurred in 2007. Laboratory-based surveillance detected a switch from dengue virus serotype 1 (DENV-1) to DENV-2. Phylogenetic analysis showed a clade replacement within DENV-2 cosmopolitan genotype, which accompanied the predominant serotype switch, and cocirculation of multiple genotypes of DENV-3.
Project description:Human beings have experienced a serious public health event as the new pneumonia (COVID-19), caused by the severe acute respiratory syndrome coronavirus has killed more than 3000 people in China, most of them elderly or people with underlying chronic diseases or immunosuppressed states. Rapid assessment and early warning are essential for outbreak analysis in response to serious public health events. This paper reviews the current model analysis methods and conclusions from both micro and macro perspectives. The establishment of a comprehensive assessment model, and the use of model analysis prediction, is very efficient for the early warning of infectious diseases. This would significantly improve global surveillance capacity, particularly in developing regions, and improve basic training in infectious diseases and molecular epidemiology.
Project description:As of 1 May 2020, there had been 6808 confirmed cases of COVID-19 in Australia. Of these, 98 had died from the disease. The epidemic had been in decline since mid-March, with 308 cases confirmed nationally since 14 April. This suggests that the collective actions of the Australian public and government authorities in response to COVID-19 were sufficiently early and assiduous to avert a public health crisis - for now. Analysing factors that contribute to individual country experiences of COVID-19, such as the intensity and timing of public health interventions, will assist in the next stage of response planning globally. We describe how the epidemic and public health response unfolded in Australia up to 13 April. We estimate that the effective reproduction number was likely below one in each Australian state since mid-March and forecast that clinical demand would remain below capacity thresholds over the forecast period (from mid-to-late April).
Project description:Introduction The COVID-19 pandemic has highlighted the need for robust data linkage systems and methods for identifying outbreaks of disease in near real-time. Objectives The primary objective of this study was to develop a real-time geospatial surveillance system to monitor the spread of COVID-19 across the UK. Methods Using self-reported app data and the Secure Anonymised Information Linkage (SAIL) Databank, we demonstrate the use of sophisticated spatial modelling for near-real-time prediction of COVID-19 prevalence at small-area resolution to inform strategic government policy areas. Results We demonstrate that using a combination of crowd-sourced app data and sophisticated geo-statistical techniques it is possible to predict hot spots of COVID-19 at fine geographic scales, nationally. We are also able to produce estimates of their precision, which is an important pre-requisite to an effective control strategy to guard against over-reaction to potentially spurious features of 'best guess' predictions. Conclusion In the UK, important emerging risk-factors such as social deprivation or ethnicity vary over small distances, hence risk needs to be modelled at fine spatial resolution to avoid aggregation bias. We demonstrate that existing geospatial statistical methods originally developed for global health applications are well-suited to this task and can be used in an anonymised databank environment, thus preserving the privacy of the individuals who contribute their data.