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1.
Am J Epidemiol ; 190(7): 1377-1385, 2021 07 01.
Article in English | MEDLINE | ID: covidwho-1387704

ABSTRACT

This primer describes the statistical uncertainty in mechanistic models and provides R code to quantify it. We begin with an overview of mechanistic models for infectious disease, and then describe the sources of statistical uncertainty in the context of a case study on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We describe the statistical uncertainty as belonging to 3 categories: data uncertainty, stochastic uncertainty, and structural uncertainty. We demonstrate how to account for each of these via statistical uncertainty measures and sensitivity analyses broadly, as well as in a specific case study on estimating the basic reproductive number, ${R}_0$, for SARS-CoV-2.


Subject(s)
COVID-19/transmission , Epidemiologic Measurements , Models, Statistical , Uncertainty , Basic Reproduction Number , Communicable Diseases , Humans , Monte Carlo Method , Pandemics , SARS-CoV-2
3.
J Travel Med ; 27(8)2020 12 23.
Article in English | MEDLINE | ID: covidwho-1059830

ABSTRACT

Data from a recent epidemiological surveillance network showed a decrease in the reported number of sexually transmitted diseases (STDs) and food-borne infections. We reflect on the possible drivers and consequences of a decrease in these transmittable infectious diseases linked to human contact in relation to social distancing due to the COVID-19 pandemic in Madrid (Spain).


Subject(s)
COVID-19 , Foodborne Diseases/epidemiology , Physical Distancing , Sexually Transmitted Diseases/epidemiology , Basic Reproduction Number/statistics & numerical data , COVID-19/epidemiology , COVID-19/prevention & control , Communicable Disease Control/methods , Communicable Disease Control/statistics & numerical data , Epidemiologic Measurements , Epidemiological Monitoring , Humans , SARS-CoV-2 , Spain/epidemiology
4.
J Travel Med ; 27(8)2020 12 23.
Article in English | MEDLINE | ID: covidwho-1059308
5.
J Travel Med ; 27(8)2020 12 23.
Article in English | MEDLINE | ID: covidwho-889576

ABSTRACT

BACKGROUND: The COVID-19 pandemic has posed an ongoing global crisis, but how the virus spread across the world remains poorly understood. This is of vital importance for informing current and future pandemic response strategies. METHODS: We performed two independent analyses, travel network-based epidemiological modelling and Bayesian phylogeographic inference, to investigate the intercontinental spread of COVID-19. RESULTS: Both approaches revealed two distinct phases of COVID-19 spread by the end of March 2020. In the first phase, COVID-19 largely circulated in China during mid-to-late January 2020 and was interrupted by containment measures in China. In the second and predominant phase extending from late February to mid-March, unrestricted movements between countries outside of China facilitated intercontinental spread, with Europe as a major source. Phylogenetic analyses also revealed that the dominant strains circulating in the USA were introduced from Europe. However, stringent restrictions on international travel across the world since late March have substantially reduced intercontinental transmission. CONCLUSIONS: Our analyses highlight that heterogeneities in international travel have shaped the spatiotemporal characteristics of the pandemic. Unrestricted travel caused a large number of COVID-19 exportations from Europe to other continents between late February and mid-March, which facilitated the COVID-19 pandemic. Targeted restrictions on international travel from countries with widespread community transmission, together with improved capacity in testing, genetic sequencing and contact tracing, can inform timely strategies for mitigating and containing ongoing and future waves of COVID-19 pandemic.


Subject(s)
Air Travel , COVID-19 , Communicable Disease Control , Disease Transmission, Infectious , Global Health/statistics & numerical data , SARS-CoV-2/isolation & purification , Air Travel/statistics & numerical data , Air Travel/trends , Bayes Theorem , COVID-19/epidemiology , COVID-19/prevention & control , COVID-19/transmission , Communicable Disease Control/methods , Communicable Disease Control/organization & administration , Disease Transmission, Infectious/prevention & control , Disease Transmission, Infectious/statistics & numerical data , Epidemiologic Measurements , Epidemiological Monitoring , Humans , Phylogeny , Spatio-Temporal Analysis
7.
Swiss Med Wkly ; 150: w20313, 2020 07 13.
Article in English | MEDLINE | ID: covidwho-651678

ABSTRACT

The reproduction number is broadly considered as a key indicator for the spreading of the COVID-19 pandemic. Its estimated value is a measure of the necessity and, eventually, effectiveness of interventions imposed in various countries. Here we present an online tool for the data-driven inference and quantification of uncertainties for the reproduction number, as well as the time points of interventions for 51 European countries. The study relied on the Bayesian calibration of the SIR model with data from reported daily infections from these countries. The model fitted the data, for most countries, without individual tuning of parameters. We also compared the results of SIR and SEIR models, which give different estimates of the reproduction number, and provided an analytical relationship between the respective numbers. We deployed a Bayesian inference framework with efficient sampling algorithms, to present a publicly available graphical user interface (https://cse-lab.ethz.ch/coronavirus) that allows the user to assess and compare predictions for pairs of European countries. The results quantified the rate of the disease’s spread before and after interventions, and provided a metric for the effectiveness of non-pharmaceutical interventions in different countries. They also indicated how geographic proximity and the times of interventions affected the progression of the epidemic.


Subject(s)
Basic Reproduction Number/statistics & numerical data , Coronavirus Infections , Disease Transmission, Infectious/statistics & numerical data , Epidemiological Monitoring , Pandemics , Pneumonia, Viral , Bayes Theorem , Betacoronavirus/isolation & purification , COVID-19 , Communicable Disease Control/methods , Communicable Disease Control/statistics & numerical data , Coronavirus Infections/epidemiology , Coronavirus Infections/prevention & control , Coronavirus Infections/transmission , Disease Transmission, Infectious/prevention & control , Epidemiologic Measurements , Europe/epidemiology , Humans , Pandemics/prevention & control , Pandemics/statistics & numerical data , Pneumonia, Viral/epidemiology , Pneumonia, Viral/prevention & control , Pneumonia, Viral/transmission , SARS-CoV-2 , Uncertainty
9.
Head Neck ; 42(7): 1539-1542, 2020 Jul.
Article in English | MEDLINE | ID: covidwho-457356

ABSTRACT

In the setting of the coronavirus disease 2019 pandemic, the concept of the disease curve has become ubiquitous in medicine and across society. Nevertheless, even among medical specialists, there are common misconception about the curve and how it affects population outcomes. This article provides a simple review of the various population dynamics at play. Principles such as the area under the curve and the threshold of capacity are discussed and simply conceptualized. Understanding the fundamental characteristics of a problem can allow us to see it with more clarity. By the end of the article, the reader will gain an effortless a sense of insight on this topic.


Subject(s)
Betacoronavirus , Coronavirus Infections/epidemiology , Epidemiologic Measurements , Pneumonia, Viral/epidemiology , Area Under Curve , COVID-19 , Coronavirus Infections/diagnosis , Coronavirus Infections/therapy , Humans , Pandemics , Pneumonia, Viral/diagnosis , Pneumonia, Viral/therapy , SARS-CoV-2 , Surge Capacity
10.
PLoS One ; 15(5): e0233875, 2020.
Article in English | MEDLINE | ID: covidwho-425364

ABSTRACT

We perform a statistical analysis for understanding the effect of the environmental temperature on the exponential growth rate of the cases infected by COVID-19 for US and Italian regions. In particular, we analyze the datasets of regional infected cases, derive the growth rates for regions characterized by a readable exponential growth phase in their evolution spread curve and plot them against the environmental temperatures averaged within the same regions, derive the relationship between temperature and growth rate, and evaluate its statistical confidence. The results clearly support the first reported statistically significant relationship of negative correlation between the average environmental temperature and exponential growth rates of the infected cases. The critical temperature, which eliminates the exponential growth, and thus the COVID-19 spread in US regions, is estimated to be TC = 86.1 ± 4.3 F0.


Subject(s)
Betacoronavirus/physiology , Coronavirus Infections/transmission , Models, Biological , Pneumonia, Viral/transmission , COVID-19 , Coronavirus Infections/epidemiology , Epidemiologic Measurements , Humans , Italy/epidemiology , Pandemics , Pneumonia, Viral/epidemiology , SARS-CoV-2 , Temperature , United States/epidemiology
11.
PLoS One ; 15(3): e0230405, 2020.
Article in English | MEDLINE | ID: covidwho-20768

ABSTRACT

Since the first suspected case of coronavirus disease-2019 (COVID-19) on December 1st, 2019, in Wuhan, Hubei Province, China, a total of 40,235 confirmed cases and 909 deaths have been reported in China up to February 10, 2020, evoking fear locally and internationally. Here, based on the publicly available epidemiological data for Hubei, China from January 11 to February 10, 2020, we provide estimates of the main epidemiological parameters. In particular, we provide an estimation of the case fatality and case recovery ratios, along with their 90% confidence intervals as the outbreak evolves. On the basis of a Susceptible-Infectious-Recovered-Dead (SIDR) model, we provide estimations of the basic reproduction number (R0), and the per day infection mortality and recovery rates. By calibrating the parameters of the SIRD model to the reported data, we also attempt to forecast the evolution of the outbreak at the epicenter three weeks ahead, i.e. until February 29. As the number of infected individuals, especially of those with asymptomatic or mild courses, is suspected to be much higher than the official numbers, which can be considered only as a subset of the actual numbers of infected and recovered cases in the total population, we have repeated the calculations under a second scenario that considers twenty times the number of confirmed infected cases and forty times the number of recovered, leaving the number of deaths unchanged. Based on the reported data, the expected value of R0 as computed considering the period from the 11th of January until the 18th of January, using the official counts of confirmed cases was found to be ∼4.6, while the one computed under the second scenario was found to be ∼3.2. Thus, based on the SIRD simulations, the estimated average value of R0 was found to be ∼2.6 based on confirmed cases and ∼2 based on the second scenario. Our forecasting flashes a note of caution for the presently unfolding outbreak in China. Based on the official counts for confirmed cases, the simulations suggest that the cumulative number of infected could reach 180,000 (with a lower bound of 45,000) by February 29. Regarding the number of deaths, simulations forecast that on the basis of the up to the 10th of February reported data, the death toll might exceed 2,700 (as a lower bound) by February 29. Our analysis further reveals a significant decline of the case fatality ratio from January 26 to which various factors may have contributed, such as the severe control measures taken in Hubei, China (e.g. quarantine and hospitalization of infected individuals), but mainly because of the fact that the actual cumulative numbers of infected and recovered cases in the population most likely are much higher than the reported ones. Thus, in a scenario where we have taken twenty times the confirmed number of infected and forty times the confirmed number of recovered cases, the case fatality ratio is around ∼0.15% in the total population. Importantly, based on this scenario, simulations suggest a slow down of the outbreak in Hubei at the end of February.


Subject(s)
Betacoronavirus , Coronavirus Infections/mortality , Data Interpretation, Statistical , Disease Outbreaks , Models, Statistical , Pneumonia, Viral/mortality , Basic Reproduction Number , COVID-19 , China/epidemiology , Epidemiologic Measurements , Forecasting , Humans , Infection Control , Mortality , Pandemics , SARS-CoV-2
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