Toward Standardizing a Lexicon of Infectious Disease Modeling Terms

Milwid, Rachael M.; Steriu, Andreea; Arino, Julien; Heffernan, Jane M.; Hyder, Ayaz; Schanzer, Dena L.; Gardner, Emma; Haworth‐Brockman, Margaret; Isfeld-Kiely, Harpa; Langley, Joanne M.; Moghadas, Seyed M.

doi:10.3389/fpubh.2016.00213

Cited by 21 publications

(13 citation statements)

References 31 publications

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“…Another important parameter of transmission dynamics is the basic reproduction number (R 0 ), which is defined as the average number of secondary cases caused by a single infectious individual in a totally susceptible population [ 18 ]. As R 0 is often estimated based on the serial interval [ 19 ], the abovementioned issues affecting previous estimates of serial interval might have affected the estimates of R 0 as well.…”

Section: Introductionmentioning

confidence: 99%

Epidemiological Parameters of COVID-19: Case Series Study

Ma¹,

Zhang²,

Zeng³

et al. 2020

J Med Internet Res

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Background The estimates of several key epidemiological parameters of the COVID-19 pandemic are often based on small sample sizes or are inaccurate for various reasons. Objective The aim of this study is to obtain more robust estimates of the incubation period, serial interval, frequency of presymptomatic transmission, and basic reproduction number (R0) of COVID-19 based on a large case series. Methods We systematically retrieved and screened 20,658 reports of laboratory-confirmed COVID-19 cases released by the health authorities of China, Japan, and Singapore. In addition, 9942 publications were retrieved from PubMed and China National Knowledge Infrastructure (CNKI) through April 8, 2020. To be eligible, a report had to contain individual data that allowed for accurate estimation of at least one parameter. Widely used models such as gamma distributions were fitted to the data sets and the results with the best-fitting values were presented. Results In total, 1591 cases were included for the final analysis. The mean incubation period (n=687) and mean serial interval (n=1015 pairs) were estimated to be 7.04 (SD 4.27) days and 6.49 (SD 4.90) days, respectively. In 40 cases (5.82%), the incubation period was longer than 14 days. In 32 infector-infectee pairs (3.15%), infectees’ symptom onsets occurred before those of infectors. Presymptomatic transmission occurred in 129 of 296 infector-infectee pairs (43.58%). R0 was estimated to be 1.85 (95% CI 1.37-2.60). Conclusions This study provides robust estimates of several epidemiological parameters of COVID-19. The findings support the current practice of 14-day quarantine of persons with potential exposure, but also suggest the need for additional measures. Presymptomatic transmission together with the asymptomatic transmission reported by previous studies highlight the importance of adequate testing, strict quarantine, and social distancing.

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Section: Introductionmentioning

confidence: 99%

Epidemiological Parameters of COVID-19: Case Series Study

Ma¹,

Zhang²,

Zeng³

et al. 2020

J Med Internet Res

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“…There are a few possible reasons why this FOI analysis identified the oldest age group as having the highest risk of infection based on model AIC. Questions remain as to whether [13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28]. Furthermore, the oldest age group has a much larger age range than the others, and therefore represents a greater cumulative risk.…”

Section: Discussionmentioning

confidence: 99%

“…The force of infection (FOI) is defined as ‘the rate at which susceptible individuals become infected per unit time’, or the probability that a susceptible individual will become infected per unit time, and depends on the number of infectious individuals in a population and their contact rate with susceptible individuals [14]. FOI can be estimated from disease prevalence data [15], and the product of FOI and the number of susceptible individuals will yield estimated incidence [16]. Previous studies on other pathogens have used prevalence data to estimate FOI and derive incidence in a population of interest [17], and FOI has been used as a metric for comparison of disease burden spatially and temporally [18, 19].…”

Section: Introductionmentioning

confidence: 99%

Force of infection of Middle East respiratory syndrome in dromedary camels in Kenya

Gardner

Kiambi²,

Sitawa

et al. 2019

Epidemiol. Infect.

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Middle East respiratory syndrome coronavirus (MERS-CoV) is a zoonotic disease transmitted from dromedary camels to people, which can result in outbreaks with human-to-human transmission. Because it is a subclinical infection in camels, epidemiological measures other than prevalence are challenging to assess. This study estimated the force of infection (FOI) of MERS-CoV in camel populations from age-stratified serological data. A cross-sectional study of MERS-CoV was conducted in Kenya from July 2016 to July 2017. Seroprevalence was stratified into four age groups: <1, 1–2, 2–3 and >3 years old. Age-independent and age-dependent linear and quadratic generalised linear models were used to estimate FOI in pastoral and ranching camel herds. Models were compared based on computed AIC values. Among pastoral herds, the age-dependent quadratic FOI was the best fit model, while the age-independent FOI was the best fit for the ranching herd data. FOI provides an indirect estimate of infection risk, which is especially valuable where direct estimates of incidence and other measures of infection are challenging to obtain. The FOIs estimated in this study provide important insight about MERS-CoV dynamics in the reservoir species, and contribute to our understanding of the zoonotic risks of this important public health threat.

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“…(dimension) variables, and matrixes created by cross-tabulated component categories of identified dimensions (Collier, LaPorte, & Seawright, 2012). Yet others have chosen to use a more simplified classification approach such as employing a lexicon to help guide disease modeling on responses to infectious disease transmission (Milwid et al, 2016) or to implement complex nutrition/obesity prevention policy, systems, and environmental change interventions in the community (Thompson, Sutton, & Kuo, 2019). While the original motivation for creating the typology was to inform the development of a CIDI of obesity intervention strategies implemented in LAC, as a research and policy tool, the broader application of this classification schema adds context and value to other decisionmaking processes of local organizations and clinical entities that must make difficult program investment choices under sparse resource conditions.…”

Section: Screening and Referralmentioning

confidence: 99%

Notes From the Field: Creating a Typology of Childhood Obesity Intervention Strategies

et al. 2019

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Classification systems can be useful for evaluating and communicating the impact of interventions. We describe how a typology was created to inform the development of a community intervention dose index (CIDI) intended to measure the strength of impacts attributed to multiple childhood obesity intervention strategies implemented in a large, diverse urban jurisdiction in the United States during 2000–2016. The categorization system was constructed via a three-stage process: (Stage 1) identify relevant constructs for categorizing intervention strategies; (Stage 2) review peer-reviewed literature and program requests for proposals to identify and integrate common attributes of intervention strategies based on Stage 1 constructs; and (Stage 3) vet the results from prior stages to develop a final version of the typology, slated for research application and for use in program improvement. The final system grouped strategies into four macrolevel and five microlevel categories. Macrolevel strategies included government/public institutional policies, infrastructure investments, and business practices. Microlevel strategies included group education, counseling, health communication and social marketing, home visitation, and screening and referral. Grouping intervention strategies in a purposeful, classified manner facilitated communications among researchers and practitioners during the gathering and quantifying of intervention data for the CIDI project and may be used to guide scarce public health resource allocation decisions.

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Toward Standardizing a Lexicon of Infectious Disease Modeling Terms

Cited by 21 publications

References 31 publications

Epidemiological Parameters of COVID-19: Case Series Study

Epidemiological Parameters of COVID-19: Case Series Study

Force of infection of Middle East respiratory syndrome in dromedary camels in Kenya

Notes From the Field: Creating a Typology of Childhood Obesity Intervention Strategies

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