Effects of Multiple Cleaning and Disinfection Interventions on Infectious Diseases in Children: A Group Randomized Trial in China

Objective To assess the effectiveness of multiple cleaning and disinfection interventions in the homes and kindergartens, in reducing gastrointestinal and respiratory illnesses of children. Methods From October 2010 to September 2011, we performed a prospective, controlled study in China. 408 children under 5 years old were recruited and group randomized into intervention and control groups. Families and kindergartens in the intervention group were provided with antibacterial products for hand hygiene and surface cleaning or disinfection for one year. Each child＇s illness symptoms and sick leave were recorded every day. Results A total of 393 children completed the study, with similar baseline demographics in each of the 2 groups. Except for abdominal pain, the odds of symptoms （fever, cough and expectoration, runny nose and nasal congestion, diarrhea）, illness （acute respiratory illness and gastrointestinal illness）, and sick leave per person each month were significantly reduced by interventions. The rates of fever, diarrhea, acute respiratory illness, gastrointestinal illness and sick leave per person per year were significantly decreased as well. Conclusion Not only the acute respiratory children were significantly reduced by multiple and gastrointestinal illness but the sick leave rate in interventions.

Revisiting classical SIR modelling in light of the COVID-19 pandemic

Background:Classical infectious disease models during epidemics have widespread usage,from predicting the probability of new infections to developing vaccination plans for informing policy decisions and public health responses.However,it is important to correctly classify reported data and understand how this impacts estimation of model parameters.The COVID-19 pandemic has provided an abundant amount of data that allow for thorough testing of disease modelling assumptions,as well as how we think about classical infectious disease modelling paradigms.Objective:We aim to assess the appropriateness of model parameter estimates and preiction results in classical infectious disease compartmental modelling frameworks given available data types(infected,active,quarantined,and recovered cases)for situations where just one data type is available to fit the model.Our main focus is on how model prediction results are dependent on data being assigned to the right model compartment.Methods:We first use simulated data to explore parameter reliability and prediction capability with three formulations of the classical Susceptible-Infected-Removed(SIR)modelling framework.We then explore two applications with reported data to assess which data and models are sufficient for reliable model parameter estimation and prediction accuracy:a classical influenza outbreak in a boarding school in England and COVID-19 data from the fall of 2020 in Missoula County,Montana,USA.Results:We demonstrated the magnitude of parameter estimation errors and subsequent prediction errors resulting from data misclassification to model compartments with simulated data.We showed that prediction accuracy in each formulation of the classical disease modelling framework was largely determined by correct data classification versus misclassification.Using a classical example of influenza epidemics in an England boarding school,we argue that the Susceptible-Infected-Quarantined-Recovered(SIQR)model is more appropriate than the commonly employed SIR model given the data collected(number of active cases).Similarly,we show in the COVID-19 disease model example that reported active cases could be used inappropriately in the SIR modelling framework if treated as infected.Conclusions:We demonstrate the role of misclassification of disease data and thus the importance of correctly classifying reported data to the proper compartment using both simulated and real data.For both a classical influenza data set and a COVID-19 case data set,we demonstrate the implications of using the“right”data in the“wrong”model.The importance of correctly classifying reported data will have downstream impacts on predictions of number of infections,as well as minimal vaccination requirements.