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On realized serial and generation intervals given control measures: The COVID-19 pandemic case.

ABSTRACT: The SARS-CoV-2 pathogen is currently spreading worldwide and its propensity for presymptomatic and asymptomatic transmission makes it difficult to control. The control measures adopted in several countries aim at isolating individuals once diagnosed, limiting their social interactions and consequently their transmission probability. These interventions, which have a strong impact on the disease dynamics, can affect the inference of the epidemiological quantities. We first present a theoretical explanation of the effect caused by non-pharmaceutical intervention measures on the mean serial and generation intervals. Then, in a simulation study, we vary the assumed efficacy of control measures and quantify the effect on the mean and variance of realized generation and serial intervals. The simulation results show that the realized serial and generation intervals both depend on control measures and their values contract according to the efficacy of the intervention strategies. Interestingly, the mean serial interval differs from the mean generation interval. The deviation between these two values depends on two factors. First, the number of undiagnosed infectious individuals. Second, the relationship between infectiousness, symptom onset and timing of isolation. Similarly, the standard deviations of realized serial and generation intervals do not coincide, with the former shorter than the latter on average. The findings of this study are directly relevant to estimates performed for the current COVID-19 pandemic. In particular, the effective reproduction number is often inferred using both daily incidence data and the generation interval. Failing to account for either contraction or mis-specification by using the serial interval could lead to biased estimates of the effective reproduction number. Consequently, this might affect the choices made by decision makers when deciding which control measures to apply based on the value of the quantity thereof.

SUBMITTER: Torneri A

PROVIDER: S-EPMC8031880 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundIn the current COVID-19 pandemic, aggressive Infection Prevention and Control (IPC) measures have been adopted to prevent health care-associated transmission of COVID-19. We evaluated the impact of a multimodal IPC strategy originally designed for the containment of COVID-19 on the rates of other hospital-acquired-infections (HAIs).MethodologyFrom February-August 2020, a multimodal IPC strategy was implemented across a large health care campus in Singapore, comprising improved segregation of patients with respiratory symptoms, universal masking and heightened adherence to Standard Precautions. The following rates of HAI were compared pre- and postpandemic: health care-associated respiratory-viral-infection (HA-RVI), methicillin-resistant Staphylococcus aureus, and CP-CRE acquisition rates, health care-facility-associated C difficile infections and device-associated HAIs.ResultsEnhanced IPC measures introduced to contain COVID-19 had the unintended positive consequence of containing HA-RVI. The cumulative incidence of HA-RVI decreased from 9.69 cases per 10,000 patient-days to 0.83 cases per 10,000 patient-days (incidence-rate-ratio = 0.08; 95% confidence interval [CI] = 0.05-0.13, P< .05). Hospital-wide MRSA acquisition rates declined significantly during the pandemic (incidence-rate-ratio = 0.54, 95% CI = 0.46-0.64, P< .05), together with central-line-associated-bloodstream infection rates (incidence-rate-ratio = 0.24, 95% CI = 0.07-0.57, P< .05); likely due to increased compliance with Standard Precautions. Despite the disruption caused by the pandemic, there was no increase in CP-CRE acquisition, and rates of other HAIs remained stable.ConclusionsMultimodal IPC strategies can be implemented at scale to successfully mitigate health care-associated transmission of RVIs. Good adherence to personal-protective-equipment and hand hygiene kept other HAI rates stable even during an ongoing pandemic where respiratory infections were prioritized for interventions.

Project description:ObjectivesTo comprehensively map the existing evidence assessing the impact of travel-related control measures for containment of the SARS-CoV-2/COVID-19 pandemic.DesignRapid evidence map.Data sourcesMEDLINE, Embase and Web of Science, and COVID-19 specific databases offered by the US Centers for Disease Control and Prevention and the WHO.Eligibility criteriaWe included studies in human populations susceptible to SARS-CoV-2/COVID-19, SARS-CoV-1/severe acute respiratory syndrome, Middle East respiratory syndrome coronavirus/Middle East respiratory syndrome or influenza. Interventions of interest were travel-related control measures affecting travel across national or subnational borders. Outcomes of interest included infectious disease, screening, other health, economic and social outcomes. We considered all empirical studies that quantitatively evaluate impact available in Armenian, English, French, German, Italian and Russian based on the team's language capacities.Data extraction and synthesisWe extracted data from included studies in a standardised manner and mapped them to a priori and (one) post hoc defined categories.ResultsWe included 122 studies assessing travel-related control measures. These studies were undertaken across the globe, most in the Western Pacific region (n=71). A large proportion of studies focused on COVID-19 (n=59), but a number of studies also examined SARS, MERS and influenza. We identified studies on border closures (n=3), entry/exit screening (n=31), travel-related quarantine (n=6), travel bans (n=8) and travel restrictions (n=25). Many addressed a bundle of travel-related control measures (n=49). Most studies assessed infectious disease (n=98) and/or screening-related (n=25) outcomes; we found only limited evidence on economic and social outcomes. Studies applied numerous methods, both inferential and descriptive in nature, ranging from simple observational methods to complex modelling techniques.ConclusionsWe identified a heterogeneous and complex evidence base on travel-related control measures. While this map is not sufficient to assess the effectiveness of different measures, it outlines aspects regarding interventions and outcomes, as well as study methodology and reporting that could inform future research and evidence synthesis.

Project description:BackgroundCOVID-19 pandemic is disaster to public health worldwide. Better perspective on COVID's features early in its course-prior to the development of vaccines and widespread variants-may prove useful in the understanding of future pandemics. Ontology provides a standardized integrative method for knowledge modeling and computer-assisted reasoning. In this study, we systematically extracted and analyzed clinical phenotypes and comorbidities in COVID-19 patients found at different countries and regions during the early pandemic using an ontology-based bioinformatics approach, with the aim to identify new insights and hidden patterns of the COVID-19 symptoms.ResultsA total of 48 research articles reporting analysis of first-hand clinical data from over 40,000 COVID-19 patients were surveyed. The patients studied therein were diagnosed with COVID-19 before May 2020. A total of 18 commonly-occurring phenotypes in these COVID-19 patients were first identified and then classified into different hierarchical groups based on the Human Phenotype Ontology (HPO). This meta-analytic approach revealed that fever, cough, and the loss of smell and taste were ranked as the most commonly-occurring phenotype in China, the US, and Italy, respectively. We also found that the patients from Europe and the US appeared to have more frequent occurrence of many nervous and abdominal symptom phenotypes (e.g., loss of smell, loss of taste, and diarrhea) than patients from China during the early pandemic. A total of 22 comorbidities, such as diabetes and kidney failure, were found to commonly exist in COVID-19 patients and positively correlated with the severity of the disease. The knowledge learned from the study was further modeled and represented in the Coronavirus Infectious Disease Ontology (CIDO), supporting semantic queries and analysis. Furthermore, also considering the symptoms caused by new viral variants at the later stages, a spiral model hypothesis was proposed to address the changes of specific symptoms during different stages of the pandemic.ConclusionsDifferential patterns of symptoms in COVID-19 patients were found given different locations, time, and comorbidity types during the early pandemic. The ontology-based informatics provides a unique approach to systematically model, represent, and analyze COVID-19 symptoms, comorbidities, and the factors that influence the disease outcomes.

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On realized serial and generation intervals given control measures: The COVID-19 pandemic case.

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