Abstract:Background
On 11
th
March 2020, the World Health Organization declared COVID-19 as Pandemic. The estimation of transmission dynamics in the initial days of the outbreak of any infectious disease is crucial to control its spread in a new area. The serial interval is one of the significant epidemiological measures that determine the spread of infectious disease. It is the time interval between the onset of symptoms in the primary and secondary case.
Objectiv… Show more
“… Fig. 1 The declining probabilities over time, expressed in terms of full viral generation durations ( g ), which each last approximately two weeks but overlap so they are separated by a mean serial interval of just under one week for both SARS-CoV-1 and SARS-CoV-2 ( Bi et al, 2020 ; Cheng et al, 2020 ; He, Zhao, Li, et al, 2020 ; Rai et al, 2021 ; Wilder-Smith et al, 2020 ; Zhao, 2020 ), for transmission chains seeded by single individual primary cases remain unconfirmed by routine passive surveillance of self-reporting symptomatic cases ( P uc,g ), assuming a range of values for the effective reproductive number in the absence of any case and contact management intervention ( R e ). Predicted values for P uc,g were calculated using equations (1)–(4 ).…”
Section: Resultsmentioning
confidence: 99%
“…Here, that baseline reproductive number specifies an intervention scenario lacking contact tracing and management for confirmed cases identified through routine, symptom-based passive surveillance at health facilities and testing centres. After a given number of complete but overlapping viral generation ( g ) durations of approximately two weeks, each separated by a mean serial interval of just under one week ( Bi et al, 2020 ; Cheng et al, 2020 ; He, Zhao, Li, et al, 2020 ; Rai et al, 2021 ; Wilder-Smith et al, 2020 ; Zhao, 2020 ), the relative size of the F 1 viral population in the absence of any contact tracing and management intervention ( V g ), when compared to the original F 0 population that seeded the outbreak, may be calculated as follows: …”
Section: Methodsmentioning
confidence: 99%
“…While the incubation periods and natural serial intervals of these two coronaviruses are similar, both are slightly shorter for SARS-CoV-2 ( Bi et al, 2020 ; Cheng et al, 2020 ; He, Zhao, Li, et al, 2020 ; Rai, Shukla, & Dwivedi, 2021 ; Wilder-Smith et al, 2020 ; Zhao, 2020 ). More importantly, the viral shedding patterns of SARS-CoV-2 more closely resemble those of influenza than SARS-CoV-1 ( He, Zhao, Li, et al, 2020 ), so it has a far shorter latent period that can be as brief as 3 days or less ( Ali et al, 2020 ; Cheng et al, 2020 ; He, Zhao, Li, et al, 2020 ; Zhao, 2020 ).…”
Severe Acute Respiratory Syndrome Coronavirus 1 (SARS-CoV-1) infections almost always caused overt symptoms, so effective case and contact management enabled its effective eradication within months. However, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) usually causes only mild symptoms, so transmission chains may grow to include several individuals before at least one index case becomes ill enough to self-report for diagnosis and care. Here, simple mathematical models were developed to evaluate the implications of delayed index case detection for retrospective contact tracing and management responses. Specifically, these simulations illustrate how: (1) Contact tracing and management may effectively contain most but not all large SARS-CoV-2 clusters arising at foci with high reproduction numbers because rapidly expanding transmission chains ensure at least one overtly symptomatic index case occurs within two viral generations a week or less apart. (2) However, lower reproduction numbers give rise to thinner transmission chains extending through longer sequences of non-reporting asymptomatic and paucisymptomatic individuals, often spanning three or more viral generations (≥2 weeks of transmission) before an overtly symptomatic index case occurs. (3) Consequently, it is not always possible to fully trace and contain such long, thin transmission chains, so the community transmission they give rise to is underrepresented in surveillance data. (4) Wherever surveillance systems are weak and/or transmission proceeds within population groups with lower rates of overt clinical symptoms and/or self-reporting, case and contact management effectiveness may be more severely limited, even at the higher reproduction numbers associated with larger outbreaks. (5) Because passive surveillance platforms may be especially slow to detect the thinner transmission chains that occur at low reproduction numbers, establishing satisfactory confidence of elimination may require that no confirmed cases are detected for two full months, throughout which presumptive preventative measures must be maintained to ensure complete collapse of undetected residual transmission. (6) Greater scope exists for overcoming these limitations by enhancing field surveillance for new suspected cases than by improving diagnostic test sensitivity. (7) While population-wide active surveillance may enable complete traceability and containment, this goal may also be achievable through enhanced passive surveillance for paucisymptomatic infections, combining readily accessible decentralized testing with population hypersensitization to self-reporting with mild symptoms. Containment and elimination of SARS-CoV-2 will rely far more upon presumptive, population-wide prevention measures than was necessary for SARS-CoV-1, necessitating greater ambition, political will, investment, public support, persistence and patience. Nevertheless, case and contact management may be invaluable for at least partially containing SARS-CoV-2 transmission, especially la...
“… Fig. 1 The declining probabilities over time, expressed in terms of full viral generation durations ( g ), which each last approximately two weeks but overlap so they are separated by a mean serial interval of just under one week for both SARS-CoV-1 and SARS-CoV-2 ( Bi et al, 2020 ; Cheng et al, 2020 ; He, Zhao, Li, et al, 2020 ; Rai et al, 2021 ; Wilder-Smith et al, 2020 ; Zhao, 2020 ), for transmission chains seeded by single individual primary cases remain unconfirmed by routine passive surveillance of self-reporting symptomatic cases ( P uc,g ), assuming a range of values for the effective reproductive number in the absence of any case and contact management intervention ( R e ). Predicted values for P uc,g were calculated using equations (1)–(4 ).…”
Section: Resultsmentioning
confidence: 99%
“…Here, that baseline reproductive number specifies an intervention scenario lacking contact tracing and management for confirmed cases identified through routine, symptom-based passive surveillance at health facilities and testing centres. After a given number of complete but overlapping viral generation ( g ) durations of approximately two weeks, each separated by a mean serial interval of just under one week ( Bi et al, 2020 ; Cheng et al, 2020 ; He, Zhao, Li, et al, 2020 ; Rai et al, 2021 ; Wilder-Smith et al, 2020 ; Zhao, 2020 ), the relative size of the F 1 viral population in the absence of any contact tracing and management intervention ( V g ), when compared to the original F 0 population that seeded the outbreak, may be calculated as follows: …”
Section: Methodsmentioning
confidence: 99%
“…While the incubation periods and natural serial intervals of these two coronaviruses are similar, both are slightly shorter for SARS-CoV-2 ( Bi et al, 2020 ; Cheng et al, 2020 ; He, Zhao, Li, et al, 2020 ; Rai, Shukla, & Dwivedi, 2021 ; Wilder-Smith et al, 2020 ; Zhao, 2020 ). More importantly, the viral shedding patterns of SARS-CoV-2 more closely resemble those of influenza than SARS-CoV-1 ( He, Zhao, Li, et al, 2020 ), so it has a far shorter latent period that can be as brief as 3 days or less ( Ali et al, 2020 ; Cheng et al, 2020 ; He, Zhao, Li, et al, 2020 ; Zhao, 2020 ).…”
Severe Acute Respiratory Syndrome Coronavirus 1 (SARS-CoV-1) infections almost always caused overt symptoms, so effective case and contact management enabled its effective eradication within months. However, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) usually causes only mild symptoms, so transmission chains may grow to include several individuals before at least one index case becomes ill enough to self-report for diagnosis and care. Here, simple mathematical models were developed to evaluate the implications of delayed index case detection for retrospective contact tracing and management responses. Specifically, these simulations illustrate how: (1) Contact tracing and management may effectively contain most but not all large SARS-CoV-2 clusters arising at foci with high reproduction numbers because rapidly expanding transmission chains ensure at least one overtly symptomatic index case occurs within two viral generations a week or less apart. (2) However, lower reproduction numbers give rise to thinner transmission chains extending through longer sequences of non-reporting asymptomatic and paucisymptomatic individuals, often spanning three or more viral generations (≥2 weeks of transmission) before an overtly symptomatic index case occurs. (3) Consequently, it is not always possible to fully trace and contain such long, thin transmission chains, so the community transmission they give rise to is underrepresented in surveillance data. (4) Wherever surveillance systems are weak and/or transmission proceeds within population groups with lower rates of overt clinical symptoms and/or self-reporting, case and contact management effectiveness may be more severely limited, even at the higher reproduction numbers associated with larger outbreaks. (5) Because passive surveillance platforms may be especially slow to detect the thinner transmission chains that occur at low reproduction numbers, establishing satisfactory confidence of elimination may require that no confirmed cases are detected for two full months, throughout which presumptive preventative measures must be maintained to ensure complete collapse of undetected residual transmission. (6) Greater scope exists for overcoming these limitations by enhancing field surveillance for new suspected cases than by improving diagnostic test sensitivity. (7) While population-wide active surveillance may enable complete traceability and containment, this goal may also be achievable through enhanced passive surveillance for paucisymptomatic infections, combining readily accessible decentralized testing with population hypersensitization to self-reporting with mild symptoms. Containment and elimination of SARS-CoV-2 will rely far more upon presumptive, population-wide prevention measures than was necessary for SARS-CoV-1, necessitating greater ambition, political will, investment, public support, persistence and patience. Nevertheless, case and contact management may be invaluable for at least partially containing SARS-CoV-2 transmission, especially la...
“… Estimated instantaneous reproduction number R t based on daily reported cases and a mean 5.2-day serial interval (95% credible interval: 4.4–6.0 [ 14 ]), Brussels region, Belgium, 1 August–12 November 2020 (n = 63,838) …”
Section: Effect Of the Number Of Reported Contacts Per Case On Sars-cmentioning
To evaluate the effect of physical distancing and school reopening in Brussels between August and November 2020, we monitored changes in the number of reported contacts per SARS-CoV-2 case and associated SARS-CoV-2 transmission. The second COVID-19 pandemic wave in Brussels was the result of increased social contact across all ages following school reopening. Physical distancing measures including closure of bars and restaurants, and limiting close contacts, while primary and secondary schools remained open, reduced social mixing and controlled SARS-CoV-2 transmission.
“…Before explaining our assumption on the duration of infectiousness, we note that we used 6.6 days as the serial interval, which was supported by the early estimates available in the literature at the time of our study [4,6,8,9] and was relevant to the local context that we were trying to model. Subsequent estimates of serial interval were slightly lower (around 5.5 days) due to faster identification of cases and contact tracing efforts [7,[10][11][12][13].…”
Beginning in March 2020, the United States emerged as the global epicenter for COVID-19 cases with little to guide policy response in the absence of extensive data available for reliable epidemiological modeling in the early phases of the pandemic. In the ensuing weeks, American jurisdictions attempted to manage disease spread on a regional basis using non-pharmaceutical interventions (i.e., social distancing), as uneven disease burden across the expansive geography of the United States exerted different implications for policy management in different regions. While Arizona policymakers relied initially on state-by-state national modeling projections from different groups outside of the state, we sought to create a state-specific model using a mathematical framework that ties disease surveillance with the future burden on Arizona’s healthcare system. Our framework uses a compartmental system dynamics model using a SEIRD framework that accounts for multiple types of disease manifestations for the COVID-19 infection, as well as the observed time delay in epidemiological findings following public policy enactments. We use a compartment initialization logic coupled with a fitting technique to construct projections for key metrics to guide public health policy, including exposures, infections, hospitalizations, and deaths under a variety of social reopening scenarios. Our approach makes use of X-factor fitting and backcasting methods to construct meaningful and reliable models with minimal available data in order to provide timely policy guidance in the early phases of a pandemic.
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