Examining geographical disparities in the incubation period of the COVID-19 infected cases in Shenzhen and Hefei, China

Abstract

Background Current studies on the COVID-19 depicted a general incubation period distribution and did not examine whether the incubation period distribution varies across patients living in different geographical locations with varying environmental attributes. Profiling the incubation distributions geographically help to determine the appropriate quarantine duration for different regions. Methods This retrospective study mainly applied big data analytics and methodology, using the publicly accessible clinical report for patients (n = 543) confirmed as infected in Shenzhen and Hefei, China. Based on 217 patients on whom the incubation period could be identified by the epidemiological method. Statistical and econometric methods were employed to investigate how the incubation distributions varied between infected cases reported in Shenzhen and Hefei. Results The median incubation period of the COVID-19 for all the 217 infected patients was 8 days (95% CI 7 to 9), while median values were 9 days in Shenzhen and 4 days in Hefei. The incubation period probably has an inverse U-shaped association with the meteorological temperature. The warmer condition in the winter of Shenzhen, average environmental temperature between 10 °C to 15 °C, may decrease viral virulence and result in more extended incubation periods. Conclusion Case studies of the COVID-19 outbreak in Shenzhen and Hefei indicated that the incubation period of COVID-19 had exhibited evident geographical disparities, although the pathological causality between meteorological conditions and incubation period deserves further investigation. Methodologies based on big data released by local public health authorities are applicable for identifying incubation period and relevant epidemiological research.

Incubation Period of COVID-19 Caused by Unique SARS-CoV-2 Strains

“…We identified 5012 records through PubMed, EMBASE, and Science Direct database searches, and documented the study selection process in a flowchart and showed the total numbers of retrieved references and the numbers of included and excluded studies (Figure 1). Based on the inclusion and exclusion criteria, 142 articles (8112 patients) were selected for analysis …”

Assessing changes in incubation period, serial interval, and generation time of SARS-CoV-2 variants of concern: a systematic review and meta-analysis

“…Quality assessment (Additional file 1 : Table S4 [ 9 , 10 , 12 , 13 , 21 , 22 , 26 – 32 , 34 , 37 – 40 , 42 – 60 , 62 – 65 , 67 – 71 , 73 , 74 , 76 , 78 – 86 , 89 , 90 , 92 – 94 , 97 – 99 , 101 – 107 , 109 – 115 , 117 – 121 , 123 – 128 , 130 , 131 , 133 , 136 – 139 , 141 – 153 , 155 – 161 , 164 – 166 ]) indicated that 1 study provided a precise exposure window for cases and identification of the potential infector(s). Sixty-eight studies included a well-characterized cohort of individuals that were comparable with the population and provided precise estimates for the symptom onset window for themselves and their potential infector(s).…”

“…3 , Additional file 1 : Fig. S1 [ 9 , 12 , 21 , 26 , 28 , 30 – 32 , 34 , 38 – 40 , 42 , 43 , 47 , 51 , 52 , 58 – 60 , 62 , 63 , 65 , 67 , 70 , 73 , 74 , 76 , 78 , 80 , 81 , 83 , 85 , 86 , 92 , 97 , 98 , 103 – 107 , 109 – 115 , 117 – 119 , 130 , 131 , 133 , 136 – 139 , 142 , 143 , 155 , 156 , 165 , 166 ]). Two studies did not specify the Omicron subvariant, and their pooled mean was 3.29 days (95% CI: 2.98–3.59 days); by pooling the estimates of the Omicron subvariants, the mean incubation period was 3.63 days (95% CI: 3.25–4.02 days).…”

“…Our results suggested no potential publication bias in the included studies ( p -value: 0.120) (Additional file 1 : Fig. S2 [ 9 , 12 , 21 , 26 , 28 , 30 – 32 , 34 , 38 – 40 , 42 , 43 , 47 , 51 , 52 , 58 – 60 , 62 , 63 , 65 , 67 , 69 , 70 , 73 , 74 , 76 , 78 , 80 , 81 , 83 , 85 , 86 , 92 , 97 , 98 , 103 – 107 , 109 – 115 , 117 – 119 , 130 , 131 , 133 , 136 – 139 , 142 , 143 , 155 , 156 , 165 , 166 ]).

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Semiparametric regression analysis of doubly-censored data with applications to incubation period estimation

“…( 2020 ) found that older subjects have longer median incubation period than younger subjects, and Xiao et al. ( 2021 ) suggested that the incubation period distribution is associated with the meteorological temperature of the area. A regression framework allows us to more precisely evaluate the incubation period distribution for given subpopulations.…”