scholarly journals Transmission of SARS-CoV-2 in Georgia, USA, 2020

2020 ◽  
Author(s):  
Kamalich Muniz-Rodriguez ◽  
Gerardo Chowell ◽  
Jessica S. Schwind ◽  
Randall Ford ◽  
Sylvia K. Ofori ◽  
...  
Keyword(s):  
Growth Phase ◽  
Exponential Growth ◽  
Growth Pattern ◽  
Cumulative Incidence ◽  
The State ◽  
Control Measures ◽  
Reproductive Number ◽  
Initial Growth ◽  
Social Distancing ◽  
Dougherty County

ABSTRACTObjectiveSARS-CoV-2 has significantly impacted Georgia, USA including two major hotspots, Metro Atlanta and Dougherty County in southwestern Georgia. With government deliberations about relaxing social distancing measures, it is important to understand the trajectory of the epidemic in the state of Georgia.MethodsWe collected daily cumulative incidence of confirmed COVID-19 cases in Georgia. We estimated the reproductive number (Re) of the COVID-19 epidemic on April 18 and May 2 by characterizing the initial growth phase of the epidemic using the generalized-growth model.ResultsThe data presents a sub-exponential growth pattern in the cumulative incidence curves. On April 18, 2020, Re was estimated as 1.20 (95% CI: 1.10, 1.20) for the state of Georgia, 1.10 (95% CI: 1.00, 1.20) for Dougherty County, and 1.20 (95% CI: 1.10, 1.20) for Metro Atlanta. Extending our analysis to May 2, 2020, Re estimates decreased to 1.10 (95% CI: 1.10, 1.10) for the state of Georgia, 1.00 (95% CI: 1.00, 1.10) for Dougherty County, and 1.10 (95% CI: 1.10, 1.10) for Metro Atlanta.ConclusionsTransmission appeared to be decreasing after the implementation of social distancing measures. However, these results should be interpreted with caution when considering relaxing control measures due to low testing rates.

scholarly journals Characterizing super-spreading events and age-specific infectivity of COVID-19 transmission in Georgia, USA

2020 ◽  
Author(s):  
Max SY Lau ◽  
Bryan Grenfell ◽  
Kristin Nelson ◽  
Ben Lopman
Keyword(s):  
Natural History ◽  
Data Streams ◽  
The State ◽  
Control Measures ◽  
Transmission Dynamics ◽  
Reproductive Number ◽  
Social Distancing ◽  
Individual Level ◽  
Multiple Data ◽  
Multiple Data Streams

AbstractAs the current COVID-19 pandemic continues to impact countries around the globe, refining our understanding of its transmission dynamics and the effectiveness of interventions is imperative. In particular, it is essential to obtain a firmer grasp on the effect of social distancing, potential individual-level heterogeneities in transmission such as age-specific infectivity, and impact of super-spreading. To this end, it is important to exploit multiple data streams that are becoming abundantly available during the pandemic. In this paper, we formulate an individual-level spatiotemporal mechanistic framework to statistically integrate case data with geo-location data and aggregate mobility data, enabling a more granular understanding of the transmission dynamics of COVID-19. We analyze reported cases from surveillance data, between March and early May 2020, in five (urban and rural) counties in the State of Georgia USA. We estimate natural history parameters of COVID-19 and infer unobserved quantities including infection times and transmission paths using Bayesian data-augmentation techniques. First, our results show that the overall median reproductive number was 2.88 (with 95% C.I. [1.85, 4.9]) before the state-wide shelter-in-place order issued in early April, and the effective reproductive number was reduced to below 1 about two weeks by the order. Super-spreading appears to be widespread across space and time, and it may have a particularly important role in driving the outbreak in rural area and an increasing importance towards later stages of outbreaks in both urban and rural settings. Overall, about 2% of cases may have directly infected 20% of all infections. We estimate that the infected children and younger adults (<60 years old) may be 2.38 [1.30, 3.51] times more transmissible than infected elderly (>=60), and the former may be the main driver of super-spreading. Through the synthesis of multiple data streams using our transmission modelling framework, our results enforce and improve our understanding of the natural history and transmission dynamics of COVID-19. More importantly, we reveal the roles of age-specific infectivity and characterize systematic variations and associated risk factors of super-spreading. These have important implications for the planning of relaxing social distancing and, more generally, designing optimal control measures.

scholarly journals Comparative estimation of the reproduction number for pandemic influenza from daily case notification data

2006 ◽  
Vol 4 (12) ◽  
pp. 155-166 ◽  
Author(s):  
Gerardo Chowell ◽  
Hiroshi Nishiura ◽  
Luís M.A Bettencourt
Keyword(s):  
Growth Phase ◽  
Pandemic Influenza ◽  
Exponential Growth ◽  
Goodness Of Fit ◽  
Reproduction Number ◽  
Exponential Growth Phase ◽  
Initial Growth ◽  
Epidemic Curve ◽  
Spanish Flu ◽  
The City

The reproduction number, , defined as the average number of secondary cases generated by a primary case, is a crucial quantity for identifying the intensity of interventions required to control an epidemic. Current estimates of the reproduction number for seasonal influenza show wide variation and, in particular, uncertainty bounds for for the pandemic strain from 1918 to 1919 have been obtained only in a few recent studies and are yet to be fully clarified. Here, we estimate using daily case notifications during the autumn wave of the influenza pandemic (Spanish flu) in the city of San Francisco, California, from 1918 to 1919. In order to elucidate the effects from adopting different estimation approaches, four different methods are used: estimation of using the early exponential-growth rate (Method 1), a simple susceptible–exposed–infectious–recovered (SEIR) model (Method 2), a more complex SEIR-type model that accounts for asymptomatic and hospitalized cases (Method 3), and a stochastic susceptible–infectious–removed (SIR) with Bayesian estimation (Method 4) that determines the effective reproduction number at a given time t . The first three methods fit the initial exponential-growth phase of the epidemic, which was explicitly determined by the goodness-of-fit test. Moreover, Method 3 was also fitted to the whole epidemic curve. Whereas the values of obtained using the first three methods based on the initial growth phase were estimated to be 2.98 (95% confidence interval (CI): 2.73, 3.25), 2.38 (2.16, 2.60) and 2.20 (1.55, 2.84), the third method with the entire epidemic curve yielded a value of 3.53 (3.45, 3.62). This larger value could be an overestimate since the goodness-of-fit to the initial exponential phase worsened when we fitted the model to the entire epidemic curve, and because the model is established as an autonomous system without time-varying assumptions. These estimates were shown to be robust to parameter uncertainties, but the theoretical exponential-growth approximation (Method 1) shows wide uncertainty. Method 4 provided a maximum-likelihood effective reproduction number 2.10 (1.21, 2.95) using the first 17 epidemic days, which is consistent with estimates obtained from the other methods and an estimate of 2.36 (2.07, 2.65) for the entire autumn wave. We conclude that the reproduction number for pandemic influenza (Spanish flu) at the city level can be robustly assessed to lie in the range of 2.0–3.0, in broad agreement with previous estimates using distinct data.

scholarly journals Epidemiological, clinical, and public health response characteristics of a large outbreak of diphtheria among the Rohingya population in Cox’s Bazar, Bangladesh, 2017 to 2019: A retrospective study

PLoS Medicine ◽  
2021 ◽  
Vol 18 (4) ◽  
pp. e1003587
Author(s):  
Jonathan A. Polonsky ◽  
Melissa Ivey ◽  
Khadimul Anam Mazhar ◽  
Ziaur Rahman ◽  
Olivier le Polain de Waroux ◽  
...  
Keyword(s):  
Public Health ◽  
Growth Phase ◽  
Exponential Growth ◽  
Case Fatality ◽  
Cervical Lymphadenopathy ◽  
Control Measures ◽  
Exponential Growth Phase ◽  
Public Health Response ◽  
Household Contacts ◽  
Large Outbreak

Background Unrest in Myanmar in August 2017 resulted in the movement of over 700,000 Rohingya refugees to overcrowded camps in Cox’s Bazar, Bangladesh. A large outbreak of diphtheria subsequently began in this population. Methods and findings Data were collected during mass vaccination campaigns (MVCs), contact tracing activities, and from 9 Diphtheria Treatment Centers (DTCs) operated by national and international organizations. These data were used to describe the epidemiological and clinical features and the control measures to prevent transmission, during the first 2 years of the outbreak. Between November 10, 2017 and November 9, 2019, 7,064 cases were reported: 285 (4.0%) laboratory-confirmed, 3,610 (51.1%) probable, and 3,169 (44.9%) suspected cases. The crude attack rate was 51.5 cases per 10,000 person-years, and epidemic doubling time was 4.4 days (95% confidence interval [CI] 4.2–4.7) during the exponential growth phase. The median age was 10 years (range 0–85), and 3,126 (44.3%) were male. The typical symptoms were sore throat (93.5%), fever (86.0%), pseudomembrane (34.7%), and gross cervical lymphadenopathy (GCL; 30.6%). Diphtheria antitoxin (DAT) was administered to 1,062 (89.0%) out of 1,193 eligible patients, with adverse reactions following among 229 (21.6%). There were 45 deaths (case fatality ratio [CFR] 0.6%). Household contacts for 5,702 (80.7%) of 7,064 cases were successfully traced. A total of 41,452 contacts were identified, of whom 40,364 (97.4%) consented to begin chemoprophylaxis; adherence was 55.0% (N = 22,218) at 3-day follow-up. Unvaccinated household contacts were vaccinated with 3 doses (with 4-week interval), while a booster dose was administered if the primary vaccination schedule had been completed. The proportion of contacts vaccinated was 64.7% overall. Three MVC rounds were conducted, with administrative coverage varying between 88.5% and 110.4%. Pentavalent vaccine was administered to those aged 6 weeks to 6 years, while tetanus and diphtheria (Td) vaccine was administered to those aged 7 years and older. Lack of adequate diagnostic capacity to confirm cases was the main limitation, with a majority of cases unconfirmed and the proportion of true diphtheria cases unknown. Conclusions To our knowledge, this is the largest reported diphtheria outbreak in refugee settings. We observed that high population density, poor living conditions, and fast growth rate were associated with explosive expansion of the outbreak during the initial exponential growth phase. Three rounds of mass vaccinations targeting those aged 6 weeks to 14 years were associated with only modestly reduced transmission, and additional public health measures were necessary to end the outbreak. This outbreak has a long-lasting tail, with Rt oscillating at around 1 for an extended period. An adequate global DAT stockpile needs to be maintained. All populations must have access to health services and routine vaccination, and this access must be maintained during humanitarian crises.

scholarly journals Transmission dynamics and control of COVID-19 in Chile, March-October, 2020

2020 ◽  
Author(s):  
Amna Tariq ◽  
Eduardo A. Undurraga ◽  
Carla Castillo Laborde ◽  
Katia Vogt-Geisse ◽  
Ruiyan Luo ◽  
...  
Keyword(s):  
Low Income ◽  
Exponential Growth ◽  
Reproduction Number ◽  
Growth Parameter ◽  
Control Measures ◽  
Transmission Dynamics ◽  
Virus Spread ◽  
Growth Trend ◽  
Social Distancing ◽  
First Case

Since the detection of the first case of COVID-19 in Chile on March 3rd, 2020, a total of 513188 cases, including ~14302 deaths have been reported in Chile as of November 2nd, 2020. Here, we estimate the reproduction number throughout the epidemic in Chile and study the effectiveness of control interventions especially the effectiveness of lockdowns by conducting short-term forecasts based on the early transmission dynamics of COVID-19. Chile's incidence curve displays early sub-exponential growth dynamics with the deceleration of growth parameter, p, estimated at 0.8 (95% CI: 0.7, 0.8) and the reproduction number, R, estimated at 1.8 (95% CI: 1.6, 1.9). Our findings indicate that the control measures at the start of the epidemic significantly slowed down the spread of the virus. However, the relaxation of restrictions and spread of the virus in low-income neighborhoods in May led to a new surge of infections, followed by the reimposition of lockdowns in Greater Santiago and other municipalities. These measures have decelerated the virus spread with R estimated at ~0.96( 95% CI: 0.95, 0.98) as of November 2nd, 2020. The early sub-exponential growth trend (p ~0.8) of the COVID-19 epidemic transformed into a linear growth trend (p ~0.5) as of July 7th, 2020, after the reimposition of lockdowns. While the broad scale social distancing interventions have slowed the virus spread, the number of new COVID-19 cases continue to accrue, underscoring the need for persistent social distancing and active case detection and isolation efforts to maintain the epidemic under control.

scholarly journals Re-analysis of plant CO2 responses during the exponential growth phase: interactions with light, temperature, nutrients and water availability

2015 ◽  
Vol 42 (10) ◽  
pp. 989 ◽  
Author(s):  
Miko U. F. Kirschbaum ◽  
Suzanne M. Lambie
Keyword(s):  
Plant Growth ◽  
Growth Phase ◽  
Exponential Growth ◽  
Cost Effective ◽  
C3 Plants ◽  
Exponential Growth Phase ◽  
Initial Growth ◽  
Short Term ◽  
Co2 Response ◽  
Explicit Consideration

Many short-term experiments have been conducted under increasing CO2 but results have been varied and have not yet led to a conclusive quantitative understanding of the CO2 response of plant growth. This may have been partly due to a lack of explicit consideration of the positive feedback inherent in plant growth during periods of exponential growth. This feedback can increase an initial physiological enhancement of relative growth rate (RGR) into a much larger biomass enhancement. To overcome this problem, we re-analysed existing experimental data from 78 publications. We calculated the RGRs of C3 plants and their relative enhancement under elevated CO2 and derived response indices that were independent of the duration of experiments and the RGR at normal atmospheric CO2. The RGR of unstressed plants increased by 14 ± 2% under doubled CO2, with observed RGR enhancement linearly correlated with calculated photosynthetic enhancements (based on the Farquhar-von Caemmerer-Berry photosynthesis model), but at only half their numeric values. Calculated RGR enhancements did not change significantly for temperatures from 12 to 40°C, but were reduced under nutrient limitation, and were increased under water stress or low irradiance. We concluded that short-term experiments can offer simple and cost-effective insights into plant CO2 responses, provided they are analysed by calculating relative changes in RGR during the strictly exponential initial growth phase.

scholarly journals Patterns of the COVID-19 pandemic spread around the world: exponential versus power laws

2020 ◽  
Vol 17 (170) ◽  
pp. 20200518 ◽  
Author(s):  
Natalia L. Komarova ◽  
Luis M. Schang ◽  
Dominik Wodarz
Keyword(s):  
Power Law ◽  
Exponential Growth ◽  
Growth Pattern ◽  
Growth Dynamics ◽  
Power Laws ◽  
Epidemiological Data ◽  
Social Distancing ◽  
Spatially Distributed ◽  
The Usa ◽  
The Uk

We have analysed the COVID-19 epidemic data of more than 174 countries (excluding China) in the period between 22 January and 28 March 2020. We found that some countries (such as the USA, the UK and Canada) follow an exponential epidemic growth, while others (like Italy and several other European countries) show a power law like growth. Regardless of the best fitting law, many countries can be shown to follow a common trajectory that is similar to Italy (the epicentre at the time of analysis), but with varying degrees of delay. We found that countries with ‘younger’ epidemics, i.e. countries where the epidemic started more recently, tend to exhibit more exponential like behaviour, while countries that were closer behind Italy tend to follow a power law growth. We hypothesize that there is a universal growth pattern of this infection that starts off as exponential and subsequently becomes more power law like. Although it cannot be excluded that this growth pattern is a consequence of social distancing measures, an alternative explanation is that it is an intrinsic epidemic growth law, dictated by a spatially distributed community structure, where the growth in individual highly mixed communities is exponential but the longer term, local geographical spread (in the absence of global mixing) results in a power law. This is supported by computer simulations of a metapopulation model that gives rise to predictions about the growth dynamics that are consistent with correlations found in the epidemiological data. Therefore, seeing a deviation from straight exponential growth may be a natural progression of the epidemic in each country. On the practical side, this indicates that (i) even in the absence of strict social distancing interventions, exponential growth is not an accurate predictor of longer term infection spread, and (ii) a deviation from exponential spread and a reduction of estimated doubling times do not necessarily indicate successful interventions, which are instead indicated by a transition to a reduced power or by a deviation from power law behaviour.

scholarly journals The effective reproductive number (Rt) of COVID-19 and its relationship with social distancing

2020 ◽  
Author(s):  
José Alexandre Felizola Diniz-Filho ◽  
Lucas Jardim ◽  
Cristiana M. Toscano ◽  
Thiago Fernando Rangel
Keyword(s):  
Time Lag ◽  
Critical Issue ◽  
The State ◽  
Mobile Phone Data ◽  
Reproductive Number ◽  
Epidemiological Models ◽  
Social Distancing ◽  
Isolation Index ◽  
The Mean ◽  
The Relationship

AbstractThe expansion of the new coronavirus disease (COVID-19) triggered a renewed interest in epidemiological models and on how parameters can be estimated from observed data. Here we investigated the relationship between average number of transmissions though time, the reproductive number Rt, and social distancing index as reported by mobile phone data service inloco, for Goiás State, Brazil, between March and June 2020. We calculated Rt values using EpiEstim package in R-plataform for confirmed cases incidence curve. We found a correlation equal to -0.72 between Rt values and isolation index at a time lag of 8 days. This correlation is also significant for half of the cities of the State with more than 90,000 people, including the 3 largest ones (and the 7 cities with significant correlations account for 43% of the population of the State). As the Rt values were paired with center of the moving window of 7 days, the delay matches the mean incubation period of the virus. Our findings reinforce that isolation index can be an effective surrogate for modeling and epidemiological analyses and, more importantly, helpful for anticipating the need for early interventions, a critical issue in public health.

scholarly journals The impact of travel and timing in eliminating COVID-19

2020 ◽  
Vol 3 (1) ◽  
Author(s):  
Alexander F. Siegenfeld ◽  
Yaneer Bar-Yam
Keyword(s):  
Mixed Population ◽  
Control Measures ◽  
Reproductive Number ◽  
Stable Fixed Point ◽  
Level Control ◽  
Social Distancing ◽  
The Social ◽  
Community Transmission ◽  
The Impact ◽  
Travel Rate

Abstract While the spread of communicable diseases such as coronavirus disease 2019 (COVID-19) is often analyzed assuming a well-mixed population, more realistic models distinguish between transmission within and between geographic regions. A disease can be eliminated if the region-to-region reproductive number—i.e., the average number of other regions to which a single infected region will transmit the disease—is reduced to less than one. Here we show that this region-to-region reproductive number is proportional to the travel rate between regions and exponential in the length of the time-delay before region-level control measures are imposed. If, on average, infected regions (including those that become re-infected in the future) impose social distancing measures shortly after experiencing community transmission, the number of infected regions, and thus the number of regions in which such measures are required, will exponentially decrease over time. Elimination will in this case be a stable fixed point even after the social distancing measures have been lifted from most of the regions.

scholarly journals Transmission dynamics and control of COVID-19 in Chile, March-October, 2020

2021 ◽  
Vol 15 (1) ◽  
pp. e0009070
Author(s):  
Amna Tariq ◽  
Eduardo A. Undurraga ◽  
Carla Castillo Laborde ◽  
Katia Vogt-Geisse ◽  
Ruiyan Luo ◽  
...  
Keyword(s):  
Low Income ◽  
Exponential Growth ◽  
Reproduction Number ◽  
Growth Parameter ◽  
Control Measures ◽  
Transmission Dynamics ◽  
Virus Spread ◽  
Growth Trend ◽  
Social Distancing ◽  
First Case

Since the detection of the first case of COVID-19 in Chile on March 3rd, 2020, a total of 513,188 cases, including ~14,302 deaths have been reported in Chile as of November 2nd, 2020. Here, we estimate the reproduction number throughout the epidemic in Chile and study the effectiveness of control interventions especially the effectiveness of lockdowns by conducting short-term forecasts based on the early transmission dynamics of COVID-19. Chile’s incidence curve displays early sub-exponential growth dynamics with the deceleration of growth parameter, p, estimated at 0.8 (95% CI: 0.7, 0.8) and the reproduction number, R, estimated at 1.8 (95% CI: 1.6, 1.9). Our findings indicate that the control measures at the start of the epidemic significantly slowed down the spread of the virus. However, the relaxation of restrictions and spread of the virus in low-income neighborhoods in May led to a new surge of infections, followed by the reimposition of lockdowns in Greater Santiago and other municipalities. These measures have decelerated the virus spread with R estimated at ~0.96 (95% CI: 0.95, 0.98) as of November 2nd, 2020. The early sub-exponential growth trend (p ~0.8) of the COVID-19 epidemic transformed into a linear growth trend (p ~0.5) as of July 7th, 2020, after the reimposition of lockdowns. While the broad scale social distancing interventions have slowed the virus spread, the number of new COVID-19 cases continue to accrue, underscoring the need for persistent social distancing and active case detection and isolation efforts to maintain the epidemic under control.

Sign in / Sign up

Export Citation Format

Share Document