### Overview

MeSH Disease

Disease (212)

Infections (184)

Death (108)

Human Phenotype

Falls (18)

Pneumonia (11)

Cough (7)

Fever (6)

Transmission

Seroprevalence
displaying 1 - 10 records in total 437
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### Data-driven modeling and forecasting of COVID-19 outbreak for public policy making

Authors: Agus Hasan; Endah Putri; Hadi Susanto; Nuning Nuraini

doi:10.1101/2020.07.30.20165555 Date: 2020-08-02 Source: medRxiv

This paper presents a data-driven approach for COVID-19 outbreak modeling and forecasting, which can be used by public policy and decision makers to control the outbreak through Non-Pharmaceutical Interventions (NPI). First, we apply an extended Kalman filter (EKF) to a discrete-time stochastic augmented compartmental model to estimate the time-varying effective reproduction number TRANS Rt. We use daily confirmed cases TRANS, active cases, recovered cases, deceased cases, Case-Fatality-Rate (CFR), and infectious time as inputs for the model. Furthermore, we define a Transmission TRANS Index (TI) as a ratio between the instantaneous and the maximum value of the effective reproduction number TRANS. The value of TI shows the disease MESHD transmission TRANS in a contact between a susceptible and an infectious individual due to current measures such as physical distancing and lock-down relative to a normal condition. Based on the value of TI, we forecast different scenarios to see the effect of relaxing and tightening public measures. Case studies in three countries are provided to show the practicability of our approach.

### Covid-19 mortality rates in Northamptonshire UK: initial sub-regional comparisons and provisional SEIR model of disease MESHD disease spread TRANS spread

Authors: Nick Petford; Jackie Campbell

doi:10.1101/2020.07.30.20165399 Date: 2020-08-02 Source: medRxiv

We analysed mortality rates in a non-metropolitan UK subregion (Northamptonshire) to understand SARS-CoV-2 disease MESHD fatalities at sub 1000000 population levels. A numerical (SEIR) model was then developed to predict the spread of Covid-19 in Northamptonshire. A combined approach using statistically-weighted data to fit the start of the epidemic to the mortality record. Parameter estimates were then derived for the transmission TRANS rate and basic reproduction number TRANS. Age TRANS standardised mortality rates are highest in Northampton (urban) and lowest in semi-rural districts. Northamptonshire has a statistically higher Covid-19 mortality rate than for the East Midlands and England as a whole. Model outputs suggest the number of infected individuals exceed official estimates, meaning less than 40 percent of the population may require immunisation. Combining published (sub-regional) mortality rate data with deterministic models on disease MESHD disease spread TRANS spread has the potential to help public health practitioners develop bespoke mitigations, guided by local population demographics.

### Mathematical modeling of the transmission TRANS of SARS-CoV-2 '' Evaluating the impact of isolation in Sao Paulo State (Brazil) and lockdown in Spain associated with protective measures on the epidemic of covid-19

Authors: Hyun Mo Yang; Luis Pedro Lombardi Jr.; Fabio Fernandes Morato Castro; Ariana Campos Yang

doi:10.1101/2020.07.30.20165191 Date: 2020-08-01 Source: medRxiv

Coronavirus disease MESHD 2019 (covid-19), with the fatality rate in elder (60 years old or more) being much higher than young (60 years old or less) patients, was declared a pandemic by the World Health Organization on March 11, 2020. Taking into account this age TRANS-dependent fatality rate, a mathematical model considering young and elder subpopulations was formulated based on the natural history of covid-19 to study the transmission TRANS of the SARS-CoV-2. This model can be applied to study the epidemiological scenario resulting from the adoption of isolation or lockdown in many countries to control the rapid propagation of covid-19. We chose as examples the isolation adopted in Sao Paulo State (Brazil) in the early phase but not at the beginning of the epidemic, and the lockdown implemented in Spain when the number of severe covid-19 cases was increasing rapidly. Based on the data collected from Sa o Paulo State and Spain, the model parameters were evaluated and we obtained higher estimation for the basic reproduction number TRANS R0 TRANS (9.24 for Sao Paulo State, and 8 for Spain) compared to the currently accepted estimation of R0 TRANS around 3. The model allowed to explain the flattening of the epidemic curves by isolation in Sao Paulo State and lockdown in Spain when associated with the protective measures (face mask and social distancing) adopted by the population. However, a simplified mathematical model providing lower estimation for R0 TRANS did not explain the flattening of the epidemic curves. The implementation of the isolation in Sa o Paulo State before the rapidly increasing phase of the epidemic enlarged the period of the first wave of the epidemic and delayed its peak, which are the desirable results of isolation to avoid the overloading in the health care system.

### The basic reproduction number TRANS of SARS-CoV-2: a scoping review of available evidence

Authors: Ann Barber; John M Griffin; Miriam Casey; Aine Collins; Elizabeth A Lane; Quirine Ten Bosch; Mart De Jong; David Mc Evoy; Andrew W Byrne; Conor G McAloon; Francis Butler; Kevin Hunt; Simon J More

doi:10.1101/2020.07.28.20163535 Date: 2020-07-30 Source: medRxiv

Background: The transmissibility TRANS of SARS-CoV-2 determines both the ability of the virus to invade a population and the strength of intervention that would be required to contain or eliminate the spread of infection MESHD. The basic reproduction number TRANS, R0 TRANS, provides a quantitative measure of the transmission TRANS potential of a pathogen. Objective: Conduct a scoping review of the available literature providing estimates of R0 TRANS for SARS-CoV-2, provide an overview of the drivers of variation in R0 TRANS estimates and the considerations taken in the calculation of the parameter. Design: Scoping review of available literature between the 01 December 2019 and 07 May 2020. Data sources: Both peer-reviewed and pre-print articles were searched for on PubMed, Google Scholar, MedRxiv and BioRxiv. Selection criteria: Studies were selected for review if (i) the estimation of R0 TRANS represented either the initial stages of the outbreak or the initial stages of the outbreak prior to the onset of widespread population restriction (lockdown), (ii) the exact dates of the study period were provided and (iii) the study provided primary estimates of R0 TRANS. Results: A total of 20 R0 TRANS estimates were extracted from 15 studies. There was substantial variation in the estimates reported. Estimates derived from mathematical models fell HP within a wider range of 1.94-6.94 than statistical models which fell HP between the range of 2.2 to 4.4. Several studies made assumptions about the length of the infectious period TRANS which ranged from 5.8-20 days and the serial interval TRANS which ranged from 4.41-14 days. For a given set of parameters a longer duration of infectiousness or a longer serial interval TRANS equates to a higher R0 TRANS. Several studies took measures to minimise bias in early case reporting, to account for the potential occurrence of super-spreading events, and to account for early sub-exponential epidemic growth. Conclusions: The variation in reported estimates of R0 TRANS reflects the complex nature of the parameter itself, including the context (i.e. social/spatial structure), the methodology used to estimate the parameter, and model assumptions. R0 TRANS is a fundamental parameter in the study of infectious disease MESHD dynamics however it provides limited practical applicability outside of the context in which it was estimated, and should be calculated and interpreted with this in mind.

### The Role of Weather Conditions in COVID-19 Transmission TRANS: A Study of a Global Panel of 1236 Regions

Authors: Chen Zhang; Hua Liao; Eric Strol; Hui Li; Ru Li; Steen Solvang Jensen; Ying Zhang

doi:10.1101/2020.07.29.20164152 Date: 2020-07-30 Source: medRxiv

Weather condition may impact COVID-19 transmission TRANS. The effects of temperature and humidity on COVID-19 transmission TRANS are not clear due to the difficulties in separating impacts of social distancing. We collected COVID-19 data and social-economic features of 1236 regions in the world (1112 regions at the provincial level and 124 countries with small land area). Moreover, a large-scale satellite data was combined with these data with a regression analysis model to explore effects of temperature and relative humidity on COVID-19 spreading, as well as the possible transmission risk TRANS due to temperature change driven by seasonal cycles. The result showed every degree Celsius increase in average temperature appears to cause a 2.88% decrease in the fraction of new daily cases 6 days later and a 0.62 percent point decrease in the reproductive number TRANS ( R0 TRANS). Every percentage point increase in relative humidity is found to lead to a 0.19% decrease in the fraction of new daily cases and a 0.02 percent point decrease in R0 TRANS 6 days later. Further, the effect of temperature and humidity is near to linear based on our samples. Government intervention (e.g. lockdown policies) and lower population movement contributed to the decrease the new daily case ratio. The conclusions withstand several robustness checks, such as observation scales and maximum/minimum temperature. The conclusion indicates air temperature and relative humidity are shown to be negatively correlated with COVID-19 transmission TRANS throughout the world. Given the diversity in both climate and social-economic conditions, the risk of transmission TRANS varies globally and possibly amplifies existing global health inequalities. Weather conditions are not the decisive factor in COVID-19 transmission TRANS, in that government intervention as well as public awareness, could contribute to the mitigation of the spreading of the virus.

### The effective reproductive number TRANS (Rt) of COVID-19 and its relationship with social distancing

Authors: Lucas Jardim Sr.; Jose Alexandre Diniz-Filho Sr.; Thiago Fernando Rangel Sr.; Cristiana Maria Toscano II

doi:10.1101/2020.07.28.20163493 Date: 2020-07-29 Source: medRxiv

The expansion of the new coronavirus disease MESHD (COVID-19) triggered a renewed public interest in epidemiological models and on how parameters can be estimated from observed data. Here we investigated the relationship between average number of transmissions TRANS though time, the reproductive number TRANS Rt, and social distancing index as reported by mobile phone data service inloco, for Goias State, Brazil, between March and June 2020. We calculated Rt values using EpiEstim package in R-plataform for confirmed cases TRANS incidence curves. We found a correlation equal to -0.72 between Rt values for confirmed cases TRANS and isolation index at a time lag of 8 days. As the Rt values were paired with center of the moving window of 7 days, the delay matches the mean incubation period TRANS of the virus. Our findings reinforce that isolation index can be an effective surrogate for modeling and epidemiological analyses and, more importantly, can be an useful metrics for anticipating the need for early interventions, a critical issue in public health.

### Persistent heterogeneity not short-term overdispersion determines herd immunity to COVID-19

Authors: Alexei V Tkachenko; Sergei Maslov; Ahmed Elbanna; George Wong; Zachary Weiner; Nigel Goldenfeld

doi:10.1101/2020.07.26.20162420 Date: 2020-07-29 Source: medRxiv

It has become increasingly clear that the COVID-19 epidemic is characterized by overdispersion whereby the majority of the transmission TRANS is driven by a minority of infected individuals. Such a strong departure from the homogeneity assumptions of traditional well-mixed compartment model is usually hypothesized to be the result of short-term super-spreader events, such as individual's extreme rate of virus shedding at the peak of infectivity while attending a large gathering without appropriate mitigation. However, heterogeneity can also arise through long-term, or persistent variations in individual susceptibility or infectivity. Here, we show how to incorporate persistent heterogeneity into a wide class of epidemiological models, and derive a non-linear dependence of the effective reproduction number TRANS R_e on the susceptible population fraction S. Persistent heterogeneity has three important consequences compared to the effects of overdispersion: (1) It results in a major modification of the early epidemic dynamics; (2) It significantly suppresses the herd immunity threshold; (3) It significantly reduces the final size of the epidemic. We estimate social and biological contributions to persistent heterogeneity using data on real-life face-to-face contact networks and age TRANS variation of the incidence rate during the COVID-19 epidemic, and show that empirical data from the COVID-19 epidemic in New York City (NYC) and Chicago and all 50 US states provide a consistent characterization of the level of persistent heterogeneity. Our estimates suggest that the hardest-hit areas, such as NYC, are close to the persistent heterogeneity herd immunity threshold following the first wave of the epidemic, thereby limiting the spread of infection MESHD to other regions during a potential second wave of the epidemic. Our work implies that general considerations of persistent heterogeneity in addition to overdispersion act to limit the scale of pandemics.

### Using social contact data TRANS to predict and compare the impact of social distancing policies with implications for school re-opening

Authors: Ellen Brooks-Pollock; Jonathan M Read; Angela R McLean; Matt J Keeling; Leon Danon

doi:10.1101/2020.07.25.20156471 Date: 2020-07-27 Source: medRxiv

Background Social distancing measures, including school closures, are being used to control SARS-CoV-2 transmission TRANS in many countries. Once "lockdown" has driven incidence to low levels, selected activities are being permitted. Re-opening schools is a priority because of the welfare and educational impact of closures on children TRANS. However, the impact of school re-opening needs to be considered within the context of other measures. Methods We use social contact data TRANS from the UK to predict the impact of social distancing policies on the reproduction number TRANS. We calibrate our tool to the COVID-19 epidemic in the UK using publicly available death MESHD data and Google Community Mobility Reports. We focus on the impact of re-opening schools against a back-drop of wider social distancing easing. Results We demonstrate that pre-collected social contact data TRANS, combined with incidence data and Google Community Mobility Reports, is able to provide a time-varying estimate of the reproduction number TRANS (R). From an pre-control setting when R=2.7 (95%CI 2.5, 2.9), we estimate that the minimum reproduction number TRANS that can be achieved in the UK without limiting household contacts TRANS is 0.45 (95%CI:0.41-0.50); in the absence of other changes, preventing leisure contacts has a smaller impact (R=2.0,95%CI:1.8-2.4) than preventing work contacts (R=1.5,95%CI:1.4-1.7). We find that following lockdown (when R=0.7 (95% CI 0.6, 0.8)), opening primary schools in isolation has a modest impact on transmission TRANS R=0.83 (95%CI:0.77-0.90) but that high adherence to other measures is needed. Opening secondary schools as well as primary school is predicted to have a larger overall impact (R=0.95,95%CI:0.85-1.07), however transmission TRANS could still be controlled with effective contact tracing TRANS. Conclusions Our findings suggest that primary school children TRANS can return to school without compromising transmission TRANS, however other measures, such as social distancing and contract tracing TRANS, are required to control transmission TRANS if all age groups TRANS are to return to school. Our tool provides a mapping from policies to the reproduction number TRANS and can be used by policymakers to compare the impact of social-easing measures, dissect mitigation strategies and support careful localized control strategies.

### Analyzing the dominant SARS-CoV-2 transmission TRANS modes towards an ab-initio SEIR model

Authors: Swetaprovo Chaudhuri; Saptarshi Basu; Abhishek Saha

id:2007.13596v1 Date: 2020-07-27 Source: arXiv

In this work, different transmission TRANS modes of the SARS-CoV-2 virus and their role in determining the evolution of the Covid-19 pandemic are analyzed. Probability of infection MESHD caused by inhaling infectious droplets (initial, ejection diameters between 0.5-750$\mu m$) and probability of infection MESHD by the corresponding desiccated nuclei that mostly encapsulate the virions post droplet evaporation, are calculated. At typical, air-conditioned yet quiescent, large indoor space, for the average viral loading, and at early times, cough MESHD cough HP droplets of initial diameter between $10 \mu m$ and $50 \mu m$ have the highest infection MESHD probability. However, by the time they are to be inhaled, the diameters are most likely $5-6$ times smaller with respect to their initial diameters. While the initially near unity infection MESHD probability due to droplets (airborne/ballistic) rapidly decays within the first $25$s, the small yet persistent infection MESHD probability of airborne desiccated nuclei decays appreciably only by $1000$s. Combined with molecular collision theory adapted to calculate frequency of contact TRANS frequency of contact SERO between the susceptible population and the droplet/nuclei cloud, infection MESHD probabilities are used to define infection MESHD rate constants, ab-initio, leading to a SEIR model. Assuming the virus sustains equally well within the dried droplet nuclei as in the droplets, the floating nuclei leads to a stronger contribution to the corresponding rate constants with respect to the droplets, in the above-mentioned conditions. Combining both pathways, the basic reproduction number TRANS $\mathcal{R}_0$ caused by cough MESHD cough HP droplets and nuclei are calculated. Viral load, minimum infectious dose, sensitivity SERO of the virus half-life to the phase of its vector, extent of dilution of the respiratory jet/puff by the entraining air are the important factors that determine specific physical modes of transmission TRANS and the pandemic evolution.

### INDEPENDENT ASSOCIATION OF METEOROLOGICAL CHARACTERISTICS WITH INITIAL SPREAD OF COVID-19 IN INDIA

Authors: Hemant Kulkarni; Harshwardhan Vinod Khandait; Uday Wasudeorao Narlawar; Pragati G Rathod; Manju Mamtani

doi:10.1101/2020.07.20.20157784 Date: 2020-07-26 Source: medRxiv

Whether weather plays a part in the transmissibility TRANS of the novel COronaVIrus Disease MESHD-19 (COVID-19) is still not established. We tested the hypothesis that meteorological factors (air temperature, relative humidity, air pressure, wind speed and rainfall) are independently associated with transmissibility TRANS of COVID-19 quantified using the basic reproduction rate ( R0 TRANS). We used publicly available datasets on daily COVID-19 case counts (total n = 108,308), three-hourly meteorological data and community mobility data over a three-month period. Estimated R0 TRANS varied between 1.15-1.28. Mean daily air temperature (inversely) and wind speed (positively) were significantly associated with time dependent R0 TRANS, but the contribution of countrywide lockdown to variability in R0 TRANS was over three times stronger as compared to that of temperature and wind speed combined. Thus, abating temperatures and easing lockdown may concur with increased transmissibility TRANS of COVID-19.

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 All MeSH Disease Human Phenotype Transmission Seroprevalence

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