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Estimating COVID-19 cases and outbreaks on-stream through phone calls.

ABSTRACT: One of the main problems in controlling COVID-19 epidemic spread is the delay in confirming cases. Having information on changes in the epidemic evolution or outbreaks rise before laboratory-confirmation is crucial in decision making for Public Health policies. We present an algorithm to estimate on-stream the number of COVID-19 cases using the data from telephone calls to a COVID-line. By modelling the calls as background (proportional to population) plus signal (proportional to infected), we fit the calls in Province of Buenos Aires (Argentina) with coefficient of determination R ² > 0.85. This result allows us to estimate the number of cases given the number of calls from a specific district, days before the laboratory results are available. We validate the algorithm with real data. We show how to use the algorithm to track on-stream the epidemic, and present the Early Outbreak Alarm to detect outbreaks in advance of laboratory results. One key point in the developed algorithm is a detailed track of the uncertainties in the estimations, since the alarm uses the significance of the observables as a main indicator to detect an anomaly. We present the details of the explicit example in Villa Azul (Quilmes) where this tool resulted crucial to control an outbreak on time. The presented tools have been designed in urgency with the available data at the time of the development, and therefore have their limitations which we describe and discuss. We consider possible improvements on the tools, many of which are currently under development.

SUBMITTER: Alvarez E

PROVIDER: S-EPMC8074976 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:ContextDuring the COVID-19 pandemic, restrictions were imposed on visits in hospitals in the province of Quebec, Canada in an effort to reduce the risk of viral exposure by minimizing face-to-face contact in order to protect patients, visitors and staff. These measures led to social isolation for patients. In order to reduce this isolation, CHUM (the Centre hospitalier de l'Université de Montréal, a teaching hospital) shifted from in-person visits to courtesy telephone calls delivered by volunteers from CHUM's Volunteers, Recreation and Leisure Department.ObjectivesTo study: (1) the contribution made by these calls to reducing isolation and their limitations, (2) how the calls can be improved, and (3) whether they should be maintained, based on the views of patients and volunteers.MethodologyThis study examined two populations. The first one consisted of 189 adult patients hospitalized at CHUM who received a courtesy phone call from a volunteer and the second one consisted of the 25 CHUM volunteers who made these calls. Quantitative data were collected from patients and volunteers through questionnaires and a Smartsheet. The patient questionnaire evaluated isolation, the courtesy phone calls, the relationship of trust with the volunteer and sociodemographic questions. The volunteer questionnaire evaluated the appropriateness of the technology for the intervention, the support and training received, the impacts of the courtesy phone call on both the patients and the volunteers, an experience report and sociodemographic information. In addition, a focus group was held with 7 volunteers. Then the verbatim were transcribed and analyzed using QDA miner software.ResultsFrom April 27, 2020 to September 5, 2020 more than 11,800 calls were made, mainly concerning hospitalization conditions or home follow-ups (n = 83), and relationships with relatives, friends, and family (n = 79). For 73.6% of hospitalized patients, the courtesy calls from volunteers were a good response to their needs, and 72% of volunteers agreed. 64.5% of patients felt less isolated and 40% of volunteers felt useful.ConclusionOur data suggest that patients felt less isolated during their hospitalization because of the courtesy calls made by the volunteers, that smartphones could also be used for video calls and, finally, that maintaining this type of service seems as relevant after as during a pandemic to provide social interactions to people isolated for medical reasons.

Project description:BackgroundIsolation of cases and contact tracing is used to control outbreaks of infectious diseases, and has been used for coronavirus disease 2019 (COVID-19). Whether this strategy will achieve control depends on characteristics of both the pathogen and the response. Here we use a mathematical model to assess if isolation and contact tracing are able to control onwards transmission from imported cases of COVID-19.MethodsWe developed a stochastic transmission model, parameterised to the COVID-19 outbreak. We used the model to quantify the potential effectiveness of contact tracing and isolation of cases at controlling a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-like pathogen. We considered scenarios that varied in the number of initial cases, the basic reproduction number (R0), the delay from symptom onset to isolation, the probability that contacts were traced, the proportion of transmission that occurred before symptom onset, and the proportion of subclinical infections. We assumed isolation prevented all further transmission in the model. Outbreaks were deemed controlled if transmission ended within 12 weeks or before 5000 cases in total. We measured the success of controlling outbreaks using isolation and contact tracing, and quantified the weekly maximum number of cases traced to measure feasibility of public health effort.FindingsSimulated outbreaks starting with five initial cases, an R0 of 1·5, and 0% transmission before symptom onset could be controlled even with low contact tracing probability; however, the probability of controlling an outbreak decreased with the number of initial cases, when R0 was 2·5 or 3·5 and with more transmission before symptom onset. Across different initial numbers of cases, the majority of scenarios with an R0 of 1·5 were controllable with less than 50% of contacts successfully traced. To control the majority of outbreaks, for R0 of 2·5 more than 70% of contacts had to be traced, and for an R0 of 3·5 more than 90% of contacts had to be traced. The delay between symptom onset and isolation had the largest role in determining whether an outbreak was controllable when R0 was 1·5. For R0 values of 2·5 or 3·5, if there were 40 initial cases, contact tracing and isolation were only potentially feasible when less than 1% of transmission occurred before symptom onset.InterpretationIn most scenarios, highly effective contact tracing and case isolation is enough to control a new outbreak of COVID-19 within 3 months. The probability of control decreases with long delays from symptom onset to isolation, fewer cases ascertained by contact tracing, and increasing transmission before symptoms. This model can be modified to reflect updated transmission characteristics and more specific definitions of outbreak control to assess the potential success of local response efforts.FundingWellcome Trust, Global Challenges Research Fund, and Health Data Research UK.

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Estimating COVID-19 cases and outbreaks on-stream through phone calls.

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