COVID-19 Transmission Potential and Non-Pharmaceutical Interventions in Maine During the COVID-19 Pandemic

The study aimed to evaluate regional variation in SARS-CoV-2 transmission and assess associations between public health interventions and the time-varying reproduction number (Rt) across Maine from January 2020 to February 2023. Daily confirmed COVID-19 case counts were adjusted for reporting anomalies and delays using deconvolution. Infection counts were estimated by applying a Poisson-distributed multiplier of 4 to account for underreporting. Rt was estimated using <i>EpiEstim</i> with a 7-day sliding window from January 2020 through February 2023. The analysis of associations between Rt and public health interventions was limited to 2020, concluding just before COVID-19 vaccines became available in Maine in December 2020. <i>EpiEstim</i> was parameterized with an Omicron-specific serial interval distribution (main analysis) and an early-pandemic serial interval distribution (sensitivity analysis). Maine experienced four major COVID-19 waves. Rt values fluctuated but remained close to 1 at both the statewide and district levels. No statistically significant changes in Rt were observed in association with any interventions implemented in 2020. Our findings underscore the challenges of quantifying intervention impacts in rural settings, where low incidence and sparse data can obscure the effects of interventions. This highlights the need for enhanced surveillance tools tailored to the unique constraints of rural public health contexts.

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Publication Details

Journal
Pathogens
Published
2025-09-05
DOI
https://doi.org/10.3390/pathogens14090893
Citations
1
Primary Topic
COVID-19 epidemiological studies
Type
article
Field-Weighted Citation Impact
1.37

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article

COVID-19 Transmission Potential and Non-Pharmaceutical Interventions in Maine During the COVID-19 Pandemic

Kin On Kwok, Isaac Chun‐Hai Fung, Ina Sze-Ting Lee, Gerardo Chowell et al.
1 citations
Pathogens
COVID-19 epidemiological studies
1.37
article

COVID-19 Transmission Potential and Non-Pharmaceutical Interventions in Maine During the COVID-19 Pandemic

Kin On Kwok, Isaac Chun‐Hai Fung, Ina Sze-Ting Lee, Gerardo Chowell, Sylvia K. Ofori, Emmanuel Akowuah, Doyinsola Babatunde
article en
1 citations

Abstract

The study aimed to evaluate regional variation in SARS-CoV-2 transmission and assess associations between public health interventions and the time-varying reproduction number (Rt) across Maine from January 2020 to February 2023. Daily confirmed COVID-19 case counts were adjusted for reporting anomalies and delays using deconvolution. Infection counts were estimated by applying a Poisson-distributed multiplier of 4 to account for underreporting. Rt was estimated using <i>EpiEstim</i> with a 7-day sliding window from January 2020 through February 2023. The analysis of associations between Rt and public health interventions was limited to 2020, concluding just before COVID-19 vaccines became available in Maine in December 2020. <i>EpiEstim</i> was parameterized with an Omicron-specific serial interval distribution (main analysis) and an early-pandemic serial interval distribution (sensitivity analysis). Maine experienced four major COVID-19 waves. Rt values fluctuated but remained close to 1 at both the statewide and district levels. No statistically significant changes in Rt were observed in association with any interventions implemented in 2020. Our findings underscore the challenges of quantifying intervention impacts in rural settings, where low incidence and sparse data can obscure the effects of interventions. This highlights the need for enhanced surveillance tools tailored to the unique constraints of rural public health contexts.

PathogensVol. 14(9)
Boston University (US), Chinese University of Hong Kong (HK), Georgia State University (US), Public Health England (GB), Georgia Southern University (US), Houston Independent School District (US), Oldham Council (GB), Belmont University (US), Imperial College London (GB)
Georgia Southern University
Openalex Percentile: Top 17%
COVID-19 epidemiological studies
1.37
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