A versatile web app for identifying the drivers of COVID-19 epidemics

Abstract Background No versatile web app exists that allows epidemiologists and managers around the world to comprehensively analyze the impacts of COVID-19 mitigation. The http://covid-webapp.numerusinc.com/ web app presented here fills this gap. Methods Our web app uses a model that explicitly identifies susceptible, contact, latent, asymptomatic, symptomatic and recovered classes of individuals, and a parallel set of response classes, subject to lower pathogen-contact rates. The user inputs a CSV file of incidence and, if of interest, mortality rate data. A default set of parameters is available that can be overwritten through input or online entry, and a user-selected subset of these can be fitted to the model using maximum-likelihood estimation (MLE). Model fitting and forecasting intervals are specifiable and changes to parameters allow counterfactual and forecasting scenarios. Confidence or credible intervals can be generated using stochastic simulations, based on MLE values, or on an inputted CSV file containing Markov chain Monte Carlo (MCMC) estimates of one or more parameters. Results We illustrate the use of our web app in extracting social distancing, social relaxation, surveillance or virulence switching functions (i.e., time varying drivers) from the incidence and mortality rates of COVID-19 epidemics in Israel, South Africa, and England. The Israeli outbreak exhibits four distinct phases: initial outbreak, social distancing, social relaxation, and a second wave mitigation phase. An MCMC projection of this latter phase suggests the Israeli epidemic will continue to produce into late November an average of around 1500 new case per day, unless the population practices social-relaxation measures at least 5-fold below the level in August, which itself is 4-fold below the level at the start of July. Our analysis of the relatively late South African outbreak that became the world’s fifth largest COVID-19 epidemic in July revealed that the decline through late July and early August was characterised by a social distancing driver operating at more than twice the per-capita applicable-disease-class (pc-adc) rate of the social relaxation driver. Our analysis of the relatively early English outbreak, identified a more than 2-fold improvement in surveillance over the course of the epidemic. It also identified a pc-adc social distancing rate in early August that, though nearly four times the pc-adc social relaxation rate, appeared to barely contain a second wave that would break out if social distancing was further relaxed. Conclusion Our web app provides policy makers and health officers who have no epidemiological modelling or computer coding expertise with an invaluable tool for assessing the impacts of different outbreak mitigation policies and measures. This includes an ability to generate an epidemic-suppression or curve-flattening index that measures the intensity with which behavioural responses suppress or flatten the epidemic curve in the region under consideration.

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A Versatile Web App for Identifying the Drivers of COVID-19 Epidemics

10.1101/2020.08.06.20155804 ◽

2020 ◽

Author(s):

Wayne M. Getz ◽

Richard Salter ◽

Ludovica Luisa Vissat ◽

Nir Horvitz

Keyword(s):

Model Fitting ◽

Likelihood Estimation ◽

Stochastic Simulations ◽

Social Distancing ◽

Policies And Measures ◽

Incidence And Mortality ◽

Credible Intervals ◽

Web App ◽

Early English ◽

Second Wave

Background. No versatile web app exists that allows epidemiologists and managers around the world to fully analyze the impacts of COVID-19 mitigation. The NMB-DASA web app presented here fills this gap. Methods. Our web app uses a model that explicitly identifies susceptible, contact, latent, asymptomatic, symptomatic and recovered classes of individuals, and a parallel set of response classes, subject to lower pathogen-contact rates. The user inputs a CSV file of incidence and, if of interest, mortality rate data. A default set of parameters is available that can be overwritten through input or online entry, and a user-selected subset of these can be fitted to the model using maximum likelihood estimation (MLE). Model fitting and forecasting intervals are specifiable and changes to parameters allow counterfactual and forecasting scenarios. Confidence or credible intervals can be generated using stochastic simulations, based on MLE values, or on an inputted CSV file containing Markov chain Monte Carlo (MCMC) estimates of one or more parameters. Findings. We illustrate the use of our web app in extracting social distancing, social relaxation, surveillance or virulence switching functions (i.e., time varying drivers) from the incidence and mortality rates of COVID-19 epidemics in Israel, South Africa, and England. The Israeli outbreak exhibits four distinct phases: initial outbreak, social distancing, social relaxation, and a second wave mitigation phase. An MCMC projection of this latter phase suggests the Israeli epidemic will continue to produce into late November an average of around 1500 new case per day, unless the population practices social distancing measures at least 5-fold below the level in August, which itself is 4-fold below the level at the start of July. Our analysis of the relatively late South African outbreak that became the world's fifth largest COVID-19 epidemic in July revealed that the decline through late July and early August was characterised by a social distancing driver operating at more than twice the per-capita applicable-disease-class (pc-adc) rate of the social relaxation driver. Our analysis of the relatively early English outbreak, identified a more than 2-fold improvement in surveillance over the course of the epidemic. It also identified a pc-adc social distancing rate in early August that, though nearly four times the pc-adc social relaxation rate, appeared to barely contain a second wave that would breakout if social distancing was further relaxed. Interpretation. Our web app provides policy makers and health officers who have no epidemiological modelling or computer coding expertise with an invaluable tool for assessing the impacts of different outbreak mitigation policies and measures. To facilitate its use, mouse over instructions, user guides and training videos are available at the website.

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503. The Misappropriation of COVID-19 Related TV Ad Expenditures

Open Forum Infectious Diseases ◽

10.1093/ofid/ofaa439.696 ◽

2020 ◽

Vol 7 (Supplement_1) ◽

pp. S317-S317

Author(s):

Kartavya J Vyas

Keyword(s):

Cost Effectiveness ◽

Performance Metrics ◽

Control Measures ◽

Mortality Data ◽

Preventive Interventions ◽

Social Distancing ◽

Epidemic Curve ◽

Incidence And Mortality ◽

The Cost ◽

The U.S

Abstract Background With nearly three-fourths of the U.S. population isolated in their homes between early March and the end of May, almost all of whom regularly watch television (TV), it was no surprise that companies began to purchase airtime on major television networks to advertise (ad) their brands and showcase their empathy with the populace. But how would the coronavirus disease 2019 (COVID-19) epidemic curve have changed had these same dollars been allocated to proven preventive interventions? Methods Performance and activity metrics on all COVID-19 related TV ads that have aired in the U.S. between February 26th and June 7th, 2020, were provided by iSpot.tv, Inc., including expenditures. COVID-19 incidence and mortality data were collected from the Centers for Disease Control and Prevention (CDC). Descriptive statistics were performed to calculate total TV ad expenditures and other performance metrics across industry categories. Leveraging a previously published stochastic agent-based model that was used to assess the cost-effectiveness of non-pharmaceutical interventions to control COVID-19, the number of cases that would have been prevented had these same dollars been used for preventive interventions was calculated using cost-effectiveness ratios (CERs), the cost divided by cases prevented. Results A total of 1,513 companies purchased TV airtime during the study period, totaling approximately 1.1 million airings, 215.5 billion impressions, and $2.7 billion in expenditures; most of the expenditures were spent by the restaurant (15.9%), electronics and communications (15.4%), and vehicle (13.7%) industries. The CERs for PPE and social distancing measures were $13,856 and $29,552, respectively; therefore, had all of these TV ad dollars instead been allocated to PPE or social distancing measures, approximately 194,908 and 91,386 cases of COVID-19 may have been prevented by the end of the study period, respectively. Figure 2. COVID-19 cases prevented had TV ad expenditures been reallocated for interventions. Conclusion Americans were inundated with COVID-19 related TV ads during the early months of the pandemic and companies are now showing some signs to relent. In times of disaster, however, it is paramount that the private sector go beyond showcasing their empathy and truly become socially responsible by allocating their funds to proven prevention and control measures. Disclosures All Authors: No reported disclosures

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A Contact-Explicit Covid-19 Epidemic and Response Assessment Model

10.1101/2020.07.16.20155812 ◽

2020 ◽

Author(s):

Wayne M Getz ◽

Ludovica Luisa-Vissat ◽

Richard Salter

Keyword(s):

South Africa ◽

Latent Class ◽

Assessment Model ◽

Mortality Data ◽

Actual Number ◽

Social Distancing ◽

Pathogen Invasion ◽

Incidence And Mortality ◽

Negative Constant ◽

Web App

We formulate a refined SEIR epidemic model that explicitly includes a contact class C that either thwarts pathogen invasion and returns to the susceptible class S or progresses successively through a latent class L, a presymptomatic/asymptomatic class A, and a symptomatic class I. Individuals in both A and I may go directly to an immune class V, and in I to a dead class D. Upon this SCLAIV formulation we impose a set of drivers that can be used to develop policy to manage current Covid-19 and similar type disease outbreaks. These drivers include surveillance, social distancing (rate and efficacy), social relaxation, quarantining (linked to contact tracing), patient treatment/isolation and vaccination processes that can either be a non-negative constant or an s-shaped switching curve. The latter are defined in terms of onset and switching times, initial and final values, and abruptness of switching. We built a Covid-19 NMB-DASA web app to generate both deterministic and stochastic solutions to our SCLAIV and drivers model and use incidence and mortality data to provide both maximum-likelihood frequentist and Bayesian fitting of parameters. In the context of South African and English Covid-19 incidence data we demonstrate how to both identify and evaluate the role of drivers in ongoing outbreaks. In particular, we show that early social distancing in South Africa likely averted around 80,000 observed cases (actual number is double if only half the case are observed) during the months of June and July. We also demonstrated that incidence rates in South Africa will increase to between a conservative estimate of 15 and 30 thousand observed cases per day (again, actual number considerably higher) by the end of August if stronger social distancing measures are not effected during July and August, 2020. On different a note, we show that comparably good local optimal fits of the English data using surveillance, social distancing and social relaxation drivers can represent very different kinds of outbreaks---one with close to 90% and another with under 8% immune individuals. This latter result provides a cautionary tale of why fitting SEIR-like models to incidence or prevalence data can be extremely problematic when not anchored by other critical measures, such as levels of immunity in the population. Our presentation illustrates how our Covid-19 web app can be used by individuals without any programming skills to carry out forensic and scenario analyses in spatially contained populations such as small countries or metropolitan areas.

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The effect of lockdown on the COVID-19 epidemic in Brazil: evidence from an interrupted time series design

Cadernos de Saúde Pública ◽

10.1590/0102-311x00213920 ◽

2020 ◽

Vol 36 (10) ◽

Author(s):

Lucas Silva ◽

Dalson Figueiredo Filho ◽

Antônio Fernandes

Keyword(s):

Time Series ◽

Interrupted Time Series ◽

Positive Trend ◽

Interrupted Time Series Analysis ◽

Observation Window ◽

Social Distancing ◽

Epidemic Curve ◽

Incidence And Mortality ◽

Before And After ◽

The Impact

In response to the COVID-19 pandemic, governments worldwide have implemented social distancing policies with different levels of both enforcement and compliance. We conducted an interrupted time series analysis to estimate the impact of lockdowns on reducing the number of cases and deaths due to COVID-19 in Brazil. Official daily data was collected for four city capitals before and after their respective policies interventions based on a 14 days observation window. We estimated a segmented linear regression to evaluate the effectiveness of lockdown measures on COVID-19 incidence and mortality. The initial number of new cases and new deaths had a positive trend prior to policy change. After lockdown, a statistically significant decrease in new confirmed cases was found in all state capitals. We also found evidence that lockdown measures were likely to reverse the trend of new daily deaths due to COVID-19. In São Luís, we observed a reduction of 37.85% while in Fortaleza the decrease was 33.4% on the average difference in daily deaths if the lockdown had not been implemented. Similarly, the intervention diminished mortality in Recife by 21.76% and Belém by 16.77%. Social distancing policies can be useful tools in flattening the epidemic curve.

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Are social networking platforms the key to effective social distancing for COVID-19? (Preprint)

10.2196/preprints.19722 ◽

2020 ◽

Author(s):

Viknesh Sounderajah ◽

Hutan Ashrafian ◽

Sheraz Markar ◽

Ara Darzi

Keyword(s):

Operating System ◽

Social Networking ◽

Contact Tracing ◽

Text Data ◽

Social Distancing ◽

Web Mapping ◽

Viral Spread ◽

Behavioural Insights ◽

Mapping Software ◽

Incidence And Mortality

UNSTRUCTURED If health systems are to effectively employ social distancing measures to in response to further COVID-19 peaks, they must adopt new behavioural metrics that can supplement traditional downstream measures, such as incidence and mortality. Access to mobile digital innovations may dynamically quantify compliance to social distancing (e.g. web mapping software) as well as establish personalised real-time contact tracing of viral spread (e.g. mobile operating system infrastructure through Google-Apple partnership). In particular, text data from social networking platforms can be mined for unique behavioural insights, such as symptom tracking and perception monitoring. Platforms, such as Twitter, have shown significant promise in tracking communicable pandemics. As such, it is critical that social networking companies collaborate with each other in order to (1) enrich the data that is available for analysis, (2) promote the creation of open access datasets for researchers and (3) cultivate relationships with governments in order to affect positive change.

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Epidemiological modelling of the health and economic effects of COVID-19 control in Australia’s second wave

Journal of Public Health ◽

10.1007/s10389-021-01611-0 ◽

2021 ◽

Author(s):

R. Quentin Grafton ◽

John Parslow ◽

Tom Kompas ◽

Kathryn Glass ◽

Emily Banks

Keyword(s):

Public Health ◽

Health Outcomes ◽

Health Management ◽

Economic Effects ◽

Social Distancing ◽

The Public ◽

Long Duration ◽

Community Transmission ◽

Second Wave ◽

Detection Strategies

Abstract Background We investigated the public health and economy outcomes of different levels of social distancing to control a ‘second wave’ outbreak in Australia and identify implications for public health management of COVID-19. Methods Individual-based and compartment models were used to simulate the effects of different social distancing and detection strategies on Australian COVID-19 infections and the economy from March to July 2020. These models were used to evaluate the effects of different social distancing levels and the early relaxation of suppression measures, in terms of public health and economy outcomes. Results The models, fitted to observations up to July 2020, yielded projections consistent with subsequent cases and showed that better public health outcomes and lower economy costs occur when social distancing measures are more stringent, implemented earlier and implemented for a sufficiently long duration. Early relaxation of suppression results in worse public health outcomes and higher economy costs. Conclusions Better public health outcomes (reduced COVID-19 fatalities) are positively associated with lower economy costs and higher levels of social distancing; achieving zero community transmission lowers both public health and economy costs compared to allowing community transmission to continue; and early relaxation of social distancing increases both public health and economy costs.

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High national acceptance for COVID-19 contact tracing technologies in Taiwan (Preprint)

10.2196/preprints.31522 ◽

2021 ◽

Author(s):

Paul M. Garrett ◽

Yuwen Wang ◽

Joshua P. White ◽

Yoshihisa Kashima ◽

Simon Dennis ◽

...

Keyword(s):

Technology Acceptance ◽

Contact Tracing ◽

Virus Spread ◽

Security Measures ◽

Contributing Factors ◽

Privacy And Security ◽

Social Distancing ◽

The Public ◽

Privacy Measures ◽

Credible Intervals

BACKGROUND Governments worldwide have introduced COVID-19 tracing technologies. Taiwan, a world leader in controlling the virus’ spread, has introduced the Taiwan ‘Social Distancing App’ to facilitate COVID-19 contact tracing. However, for these technologies to be effective, they must be accepted and used by the public. OBJECTIVE Our study aimed to determine public acceptance for three hypothetical tracing technologies: a centralized Government App, a decentralized Bluetooth App (e.g., Taiwan’s Social Distancing App), and a Telecommunication tracing technology; and model what factors contributed to their acceptance. METHODS Four nationally representative surveys were conducted in April 2020 sampling 6,000 Taiwanese residents. Perceptions and impacts of COVID-19, government effectiveness, worldviews, and attitudes towards and acceptance of one-of-three hypothetical tracing technologies were assessed. RESULTS Technology acceptance was high across all hypothetical technologies (67% - 73%) and improved with additional privacy measures (82% - 88%). Bayesian modelling (using 95% highest density credible intervals) showed data sensitivity and perceived poor COVID-19 policy compliance inhibited technology acceptance. By contrast, technology benefits (e.g., returning to activities, reducing virus spread, lowering the likelihood of infection), higher education, and perceived technology privacy, security, and trust, were all contributing factors to overall acceptance. Bayesian ordinal probit models revealed higher COVID-19 concern for other people than for one’s self. CONCLUSIONS Taiwan is currently using a range of technologies to minimize the spread of COVID-19 as the country returns to normal economic and social activities. We observed high acceptance for COVID-19 tracing technologies among the Taiwanese public, a promising and necessary finding for the successful introduction of Taiwan’s new ‘Social Distancing App’. Policy makers may capitalize on this acceptance by focusing attention towards the App’s benefits, privacy and security measures, making the App’s privacy measures transparent to the public, and emphasizing App uptake and compliance among the public. CLINICALTRIAL Not applicable.

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Bayesian small-area modelling of COVID-19 cases and deaths in England and association with key risk factors: a combined space–time Susceptible-Exposed-Infected-Removed (SEIR) model

10.21203/rs.3.rs-102191/v1 ◽

2020 ◽

Author(s):

Benn Sartorius ◽

Andrew Lawson ◽

Rachel L. Pullan

Keyword(s):

Risk Factors ◽

Small Area ◽

Mortality Rates ◽

Space Time ◽

Spatiotemporal Variability ◽

Population Mobility ◽

Seir Model ◽

Social Distancing ◽

The Impact ◽

Second Wave

Abstract Background: COVID-19 caseloads in England appear have passed through a first peak, with evidence of an emerging second wave. To ensure continued response to the epidemic is most effective, it is imperative to better understand both retrospectively and prospectively the geographical evolution of COVID-19 caseloads and deaths, identify localised areas in space-time at significantly higher risk, quantify the impact of changes in localised population mobility (or movement) on caseloads, identify localised risk factors for increased mortality and project the likely course of the epidemic at small-area resolution in coming weeks.Methods: We applied a Bayesian space–time SEIR model to assess the spatiotemporal variability of COVID-19 caseloads (transmission) and deaths at small-area scale in England (Middle Layer Super Output Area [MSOA], 6791 units) and by week (using observed data from week 5 to 34), including key determinants, the modelled transmission dynamics and spatial-temporal random effects. We also estimate the number of cases and deaths at small-area resolution with uncertainty projected forward in time by MSOA (up to week 51 of 2020), the impact mobility reductions (and subsequent easing) have had on COVID-19 caseloads and quantify the impact of key socio-demographic risk factors on COVID-19 related mortality risk by MSOA.Results: Reductions in population mobility due the course of the first lockdown had a significant impact on the reduction of COVID-19 caseloads across England, however local authorities have had a varied rate of reduction in population movement which our model suggest has substantially impacted the geographic heterogeneity in caseloads at small-area scale. The steady gain in population mobility, observed from late April, appears to have contributed to a slowdown in caseload reductions towards late June and subsequent steady increase signalling the start of the second wave. MSOA with higher proportions of elderly (70+ years of age) and elderly living in deprivation, both with very distinct geographic distributions, have a significantly elevated COVID-19 mortality rates.Conclusions: While non-pharmaceutical interventions (that is, reductions in population mobility and social distancing) had a profound impact on the trajectory of the first wave of the COVID-19 outbreak in England, increased population mobility appears to have contributed to the current increase signalling the start of the second wave. A number of contiguous small-areas appear to be at a significant elevated risk of high COVID-19 transmission, many of which are also at increased risk for higher mortality rates. A geographically staggered re-introduction of intensified social distancing measures is advised and limited cross MSOA movement if the magnitude and geographic extent of the second wave is to be reduced.

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Flattening the curve and the effect of atypical events on mitigation measures in Mexico: a modeling perspective

10.1101/2020.05.21.20109678 ◽

2020 ◽

Author(s):

Mario Santana-Cibrian ◽

Manuel Adrian Acuna-Zegarra ◽

Jorge X. Velasco-Hernandez

Keyword(s):

Mitigation Measures ◽

Case Scenario ◽

Worst Case ◽

Social Distancing ◽

Epidemic Curve ◽

Effective Contact ◽

High Incidence ◽

The Government ◽

Mother’S Day ◽

Short Time

On 23 and 30 March 2020 the Mexican Federal government implemented social distancing measures to mitigate the COVID-19 epidemic. We use a mathematical model to explore atypical transmission events within the confinement period, triggered by the timing and strength of short time perturbations of social distancing. We show that social distancing measures were successful in achieving a significant reduction of the effective contact rate in the early weeks of the intervention. However, "flattening the curve" had an undesirable effect, since the epidemic peak was delayed too far, almost to the government preset day for lifting restrictions (01 June 2020). If the peak indeed occurs in late May or early June, then the events of children's day and mother's day may either generate a later peak (worst case scenario), a long plateau with relatively constant but high incidence (middle case scenario) or the same peak date as in the original baseline epidemic curve, but with a post-peak interval of slower decay.

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GOING OUT NORMALLY DURING COVID-19 PANDEMIC: INSIGHTS ABOUT THE LACK OF ADHESION TO SOCIAL DISTANCING

10.31234/osf.io/v2gd9 ◽

2021 ◽

Author(s):

Rui Mateus Joaquim ◽

André Luiz Braule Pinto ◽

Rafaela Ferreira Guatimosim ◽

Jonas Jardim de Paula ◽

Alexandre Luiz de Oliveira Serpa ◽

...

Keyword(s):

Personality Traits ◽

Emotional Stability ◽

Individual Characteristics ◽

Protective Behaviors ◽

Protective Measures ◽

Psychopathological Symptoms ◽

Social Distancing ◽

Big Five Model ◽

Second Wave

The population's adhesion to measures to ensure social distancing represents a great management challenge. Evidence has shown that social distancing is effective. However, it is challenging to separate government measures from social distancing driven by personal initiatives. Theory: It is possible that the output of protective behaviors, such as adherence to protective measures and staying in social isolation, is influenced by individual characteristics, such as personality traits or symptoms of mental distress of anxiogenic nature. We hypothesized that individuals with more expressive symptoms of fear or anxiety would have a more protective behavioral tendency in terms of risk exposure, leaving less home during the pandemic. In contrast, individuals with greater emotional stability, as they feel more secure and with a lower perception of risk, could go out more often.Material and Methods: A total of 2709 individuals from all regions of Brazil participated in the study (mean age = 42 years; 2134 women). Correlation analysis was performed to investigate the relationships between personality traits according to the big five model and Psychopathological Symptoms (BSI). Then investigate how people that go out usually differ from people that stay at home, in both symptoms and personality traits. Finally, to investigate the predictors for going out usually, we use multiple regression analysis, using gender, marital status, level of education, and personality traits. Results: During the second wave of COVID-19 in Brazil, individuals with higher emotional stability tended to leave home more than those with more expressive levels of anxiogenic dysregulation. These results reinforce the role of both personality traits and psychopathological symptoms in prophylactic behavior during COVID-19 pandemics.

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