The impact of changes in diagnostic testing practices on estimates of COVID-19 transmission in the United States

Pitzer, Virginia E.; Chitwood, Melanie H.; Havumaki, Joshua; Menzies, Nicolas A; Perniciaro, Stephanie; Warren, Joshua L.; Weinberger, Daniel M; Cohen, Ted

doi:10.1101/2020.04.20.20073338

Cited by 44 publications

(56 citation statements)

References 18 publications

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“…We showed that in most states, control strategies implemented during their "shelter-inplace" period were sufficient to contain the outbreak, defined as reducing and ultimately maintaining the effective reproductive number below 1 ( <1). However, for the majority of states, our modelling suggests that "reopening" has proceeded too rapidly Additionally, consistent with previous COVID-19 modeling studies [17][18][19] , our model uses a simple functional form to model increases in testing rate from early March to June, 2020.…”

Section: Discussionsupporting

confidence: 76%

State-level impact of social distancing and testing on COVID-19 in the United States

Chiu

Fischer

Ndeffo-Mbah

2020

Preprint

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Abstract Social distancing measures have been implemented in the United States (US) since March 2020, to mitigate the spread of SARS-CoV-2, the causative agent of COVID-19. However, by mid-May most states began relaxing these measures to support the resumption of economic activity, even as disease incidence continued to increase in many states. To evaluate the impact of relaxing social distancing restrictions on COVID-19 dynamics and control in the US, we developed a transmission dynamic model and calibrated it to US state-level COVID-19 cases and deaths from March to June 20th, 2020, using Bayesian methods. We used this model to evaluate the impact of reopening, social distancing, testing, contact tracing, and case isolation on the COVID-19 epidemic in each state. We found that using stay-at-home orders, most states were able to curtail their COVID-19 epidemic curve by reducing and achieving an effective reproductive number below 1. But by June 20th, 2020, only 19 states and the District of Columbia were on track to curtail their epidemic curve with a 75% confidence, at current levels of reopening. Of the remaining 31 states, 24 may have to double their current testing and/or contact tracing rate to curtail their epidemic curve, and seven need to further restrict social contact by 25% in addition to doubling their testing and contact tracing rates. When social distancing restrictions are being eased, greater state-level testing and contact tracing capacity remains paramount for mitigating the risk of large-scale increases in cases and deaths.

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Section: Discussionsupporting

confidence: 76%

State-level impact of social distancing and testing on COVID-19 in the United States

Chiu

Fischer

Ndeffo-Mbah

2020

Preprint

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“…Additionally, consistent with previous COVID-19 modelling studies [26][27][28] , our model uses a simple functional form to model increases in testing rate from early March to June 2020. This testing rate was estimated through model fitting to daily reported case and mortality data.…”

Section: Discussionmentioning

confidence: 84%

State-level needs for social distancing and contact tracing to contain COVID-19 in the United States

2020

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he novel coronavirus pandemic (COVID-19), first detected in Wuhan, China in December 2019, has now reached pandemic status with spread to >210 countries and territories, including the United States 1. The United States reported its first imported case of COVID-19 on 20 January 2020, arriving via an international flight from China 2. Since then the disease has spread rapidly within the country, with every state reporting confirmed cases within 3 weeks of the first reported community transmission. As of 1 August, the United States has exceeded 4.5 million cases and 150,000 deaths, heterogeneously distributed across all states 1. To date, states such as New York, New Jersey and California have borne the highest burden, with <420,000, 183,000 and 510,000 cases and 32,000, 15,000 and 9,000 deaths, respectively, while Alaska and Hawaii have each reported <4,000 cases and 25 deaths 1. COVID-19 is caused by a newly described and highly transmissible SARS-like coronavirus (SARS-CoV-2). Severe clinical outcomes have been observed in approximately 20% of symptomatic cases 3,4. There is no vaccine and no cure or approved pharmaceutical intervention for this disease, making the fight against the pandemic reliant on non-pharmaceutical interventions (NPIs). These NPIs include: (1) case-driven measures such as testing, contact tracing and isolation 5 ; (2) personal preventive measures such as hand hygiene, cough etiquette, face mask use, eye protection, physical distancing and surface cleaning, which aim to reduce the risk of transmission during contact with potentially infectious individuals 6 ; and (3) social-distancing measures to reduce interpersonal contact in the population. In the United States, social-distancing measures have included policies and guidelines to close schools and workplaces, cancel and restrict mass gatherings and group events, restrict travel, maintain physical separation from others (for example, keeping six feet apart) and stay-at-home orders 7. Non-pharmaceutical interventions and other responses to COVID-19, especially stay-at-home orders, have varied widely across states, leading to spatial and temporal variation in the timing and implementation of mitigation strategies. This variation in policies and response efforts may have contributed to the observed heterogeneity in COVID-19 morbidity and mortality across states 8. Recent studies suggest that statewide social-distancing measures have probably contributed to reducing the spread COVID-19 epidemic in the United States 9,10. Understanding the extent to which NPIs, such as social distancing, testing, contact tracing and self-quarantine, influence COVID-19 transmission in a local context is pivotal for predicting and better managing the future course of the epidemic on a state-by-state basis. This in turn will inform how these NPIs should be optimized to mitigate the spread and burden of COVID-19 while awaiting development of pharmaceutical interventions (for example, therapeutics and vaccines). After several weeks of statewide stay-at-home orders, m...

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“…As countries transition to a more liberal testing policy amid efforts to safely reopen their economies, another potential use for our model is to prioritize patients in need of testing, allocating supplies to those with a predisposition to poor outcomes. Currently, criteria for testing is highly varied, unstandardized, and rapidly evolving, as availability of testing supplies fluctuates [36]. A predictive model that prioritizes high-risk patients may allow for more appropriate allocation of resources.…”

Section: Discussionmentioning

confidence: 99%

Predicting severe outcomes in Covid-19 related illness using only patient demographics, comorbidities and symptoms

Ryan

Minc

Cáceres

et al. 2021

The American Journal of Emergency Medicine

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Objective: Development of a risk-stratification model to predict severe Covid-19 related illness, using only presenting symptoms, comorbidities and demographic data. Materials and methods: We performed a case-control study with cases being those with severe disease, defined as ICU admission, mechanical ventilation, death or discharge to hospice, and controls being those with non-severe disease. Predictor variables included patient demographics, symptoms and past medical history. Participants were 556 patients with laboratory confirmed Covid-19 and were included consecutively after presenting to the emergency department at a tertiary care center from March 1, 2020 to April 21, 2020 Results: Most common symptoms included cough (82%), dyspnea (75%), and fever/chills (77%), with 96% reporting at least one of these. Multivariable logistic regression analysis found that increasing age (adjusted odds ratio [OR], 1.05; 95% confidence interval [CI], 1.03-1.06), dyspnea (OR, 2.56; 95% CI: 1.51-4.33), male sex (OR, 1.70; 95% CI: 1.10-2.64), immunocompromised status (OR, 2.22; 95% CI: 1.17-4.16) and CKD (OR, 1.76; 95% CI: 1.01-3.06) were significant predictors of severe Covid-19 infection. Hyperlipidemia was found to be negatively associated with severe disease (OR, 0.54; 95% CI: 0.33-0.90). A predictive equation based on these variables demonstrated fair ability to discriminate severe vs non-severe outcomes using only this historical information (AUC: 0.76). Conclusions: Severe Covid-19 illness can be predicted using data that could be obtained from a remote screening. With validation, this model could possibly be used for remote triage to prioritize evaluation based on susceptibility to severe disease while avoiding unnecessary waiting room exposure. Published by Elsevier Inc. 1.2. Importance Given that most patients presenting with symptoms concerning for Covid-19 infection have ultimately been negative [8], there is a concern

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The impact of changes in diagnostic testing practices on estimates of COVID-19 transmission in the United States

Cited by 44 publications

References 18 publications

State-level impact of social distancing and testing on COVID-19 in the United States

State-level impact of social distancing and testing on COVID-19 in the United States

State-level needs for social distancing and contact tracing to contain COVID-19 in the United States

Predicting severe outcomes in Covid-19 related illness using only patient demographics, comorbidities and symptoms

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