scholarly journals Phylogenetic-based inference reveals distinct transmission dynamics of SARS-CoV-2 variant of concern Gamma and lineage P.2 in Brazil

Author(s):  
Tiago Graf ◽  
Gonzalo Bello ◽  
Felipe Gomes Naveca ◽  
Marcelo Gomes ◽  
Vanessa Leiko Oikawa Cardoso ◽  
...  

The COVID-19 epidemic in Brazil experienced two major country-wide lineage replacements, the first driven by the lineage P.2, formerly classified as variant of interest (VOI) Zeta in late 2020 and the second by the variant of concern (VOC) Gamma in early 2021. To better understand how these SARS-CoV-2 lineage turnovers occurred in Brazil, we analyzed 11,724 high-quality SARS-CoV-2 whole genomes of samples collected in different country regions between September 2020 and April 2021. Our findings indicate that the spatial dispersion of both variants in Brazil was driven by short and long-distance viral transmission. The lineage P.2 harboring Spike mutation E484K probably emerged around late July 2020 in the Rio de Janeiro (RJ) state, which contributed with most (~50%) inter-state viral disseminations, and only became locally established in most Brazilian states by October 2020. The VOC Gamma probably arose in November 2020 in the Amazonas (AM) state, which was responsible for 60-70% of the inter-state viral dissemination, and the earliest timing of community transmission of this VOC in many Brazilian states was already traced to December 2020. We estimate that variant Gamma was 1.56-3.06 more transmissible than variant P.2 co-circulating in RJ and that the median effective reproductive number (Re) of Gamma in RJ and SP states (Re = 1.59-1.91) was lower than in AM (Re = 3.55). In summary, although the epicenter of the lineage P.2 dissemination in Brazil was the heavily interconnected Southeastern region, it displayed a slower rate of spatial spread than the VOC Gamma originated in the more isolated Northern Brazilian region. Our findings also support that the VOC Gamma was more transmissible than lineage P.2, although the viral Re of the VOC varied according to the geographic context.

BMC Medicine ◽  
2021 ◽  
Vol 19 (1) ◽  
Author(s):  
K. Lokuge ◽  
E. Banks ◽  
S. Davis ◽  
L. Roberts ◽  
T. Street ◽  
...  

Abstract Background Following implementation of strong containment measures, several countries and regions have low detectable community transmission of COVID-19. We developed an efficient, rapid, and scalable surveillance strategy to detect remaining COVID-19 community cases through exhaustive identification of every active transmission chain. We identified measures to enable early detection and effective management of any reintroduction of transmission once containment measures are lifted to ensure strong containment measures do not require reinstatement. Methods We compared efficiency and sensitivity to detect community transmission chains through testing of the following: hospital cases; fever, cough and/or ARI testing at community/primary care; and asymptomatic testing; using surveillance evaluation methods and mathematical modelling, varying testing capacities, reproductive number (R) and weekly cumulative incidence of COVID-19 and non-COVID-19 respiratory symptoms using data from Australia. We assessed system requirements to identify all transmission chains and follow up all cases and primary contacts within each chain, per million population. Results Assuming 20% of cases are asymptomatic and 30% of symptomatic COVID-19 cases present for testing, with R = 2.2, a median of 14 unrecognised community cases (8 infectious) occur when a transmission chain is identified through hospital surveillance versus 7 unrecognised cases (4 infectious) through community-based surveillance. The 7 unrecognised community upstream cases are estimated to generate a further 55–77 primary contacts requiring follow-up. The unrecognised community cases rise to 10 if 50% of cases are asymptomatic. Screening asymptomatic community members cannot exhaustively identify all cases under any of the scenarios assessed. The most important determinant of testing requirements for symptomatic screening is levels of non-COVID-19 respiratory illness. If 4% of the community have respiratory symptoms, and 1% of those with symptoms have COVID-19, exhaustive symptomatic screening requires approximately 11,600 tests/million population using 1/4 pooling, with 98% of cases detected (2% missed), given 99.9% sensitivity. Even with a drop in sensitivity to 70%, pooling was more effective at detecting cases than individual testing under all scenarios examined. Conclusions Screening all acute respiratory disease in the community, in combination with exhaustive and meticulous case and contact identification and management, enables appropriate early detection and elimination of COVID-19 community transmission. An important component is identification, testing, and management of all contacts, including upstream contacts (i.e. potential sources of infection for identified cases, and their related transmission chains). Pooling allows increased case detection when testing capacity is limited, even given reduced test sensitivity. Critical to the effectiveness of all aspects of surveillance is appropriate community engagement, messaging to optimise testing uptake and compliance with other measures.


2013 ◽  
Vol 141 (8) ◽  
pp. 1572-1584 ◽  
Author(s):  
M. O. MILBRATH ◽  
I. H. SPICKNALL ◽  
J. L. ZELNER ◽  
C. L. MOE ◽  
J. N. S. EISENBERG

SUMMARYNorovirus is a common cause of gastroenteritis in all ages. Typical infections cause viral shedding periods of days to weeks, but some individuals can shed for months or years. Most norovirus risk models do not include these long-shedding individuals, and may therefore underestimate risk. We reviewed the literature for norovirus-shedding duration data and stratified these data into two distributions: regular shedding (mean 14–16 days) and long shedding (mean 105–136 days). These distributions were used to inform a norovirus transmission model that predicts the impact of long shedders. Our transmission model predicts that this subpopulation increases the outbreak potential (measured by the reproductive number) by 50–80%, the probability of an outbreak by 33%, the severity of transmission (measured by the attack rate) by 20%, and transmission duration by 100%. Characterizing and understanding shedding duration heterogeneity can provide insights into community transmission that can be useful in mitigating norovirus risk.


2018 ◽  
Vol 146 (13) ◽  
pp. 1654-1662 ◽  
Author(s):  
S. Chadsuthi ◽  
B. M. Althouse ◽  
S. Iamsirithaworn ◽  
W. Triampo ◽  
K. H. Grantz ◽  
...  

AbstractHuman movement contributes to the probability that pathogens will be introduced to new geographic locations. Here we investigate the impact of human movement on the spatial spread of Chikungunya virus (CHIKV) in Southern Thailand during a recent re-emergence. We hypothesised that human movement, population density, the presence of habitat conducive to vectors, rainfall and temperature affect the transmission of CHIKV and the spatiotemporal pattern of cases seen during the emergence. We fit metapopulation transmission models to CHIKV incidence data. The dates at which incidence in each of 151 districts in Southern Thailand exceeded specified thresholds were the target of model fits. We confronted multiple alternative models to determine which factors were most influential in the spatial spread. We considered multiple measures of spatial distance between districts and adjacency networks and also looked for evidence of long-distance translocation (LDT) events. The best fit model included driving-distance between districts, human movement, rubber plantation area and three LDT events. This work has important implications for predicting the spatial spread and targeting resources for control in future CHIKV emergences. Our modelling framework could also be adapted to other disease systems where population mobility may drive the spatial advance of outbreaks.


2020 ◽  
Vol 117 (9) ◽  
pp. 5067-5073 ◽  
Author(s):  
Rebecca Kahn ◽  
Corey M. Peak ◽  
Juan Fernández-Gracia ◽  
Alexandra Hill ◽  
Amara Jambai ◽  
...  

Forecasting the spatiotemporal spread of infectious diseases during an outbreak is an important component of epidemic response. However, it remains challenging both methodologically and with respect to data requirements, as disease spread is influenced by numerous factors, including the pathogen’s underlying transmission parameters and epidemiological dynamics, social networks and population connectivity, and environmental conditions. Here, using data from Sierra Leone, we analyze the spatiotemporal dynamics of recent cholera and Ebola outbreaks and compare and contrast the spread of these two pathogens in the same population. We develop a simulation model of the spatial spread of an epidemic in order to examine the impact of a pathogen’s incubation period on the dynamics of spread and the predictability of outbreaks. We find that differences in the incubation period alone can determine the limits of predictability for diseases with different natural history, both empirically and in our simulations. Our results show that diseases with longer incubation periods, such as Ebola, where infected individuals can travel farther before becoming infectious, result in more long-distance sparking events and less predictable disease trajectories, as compared to the more predictable wave-like spread of diseases with shorter incubation periods, such as cholera.


2021 ◽  
Vol 17 (7) ◽  
pp. e1009174
Author(s):  
Kelly Charniga ◽  
Zulma M. Cucunubá ◽  
Marcela Mercado ◽  
Franklyn Prieto ◽  
Martha Ospina ◽  
...  

Zika virus (ZIKV) and chikungunya virus (CHIKV) were recently introduced into the Americas resulting in significant disease burdens. Understanding their spatial and temporal dynamics at the subnational level is key to informing surveillance and preparedness for future epidemics. We analyzed anonymized line list data on approximately 105,000 Zika virus disease and 412,000 chikungunya fever suspected and laboratory-confirmed cases during the 2014–2017 epidemics. We first determined the week of invasion in each city. Out of 1,122, 288 cities met criteria for epidemic invasion by ZIKA and 338 cities by CHIKV. We analyzed risk factors for invasion using linear and logistic regression models. We also estimated that the geographic origin of both epidemics was located in Barranquilla, north Colombia. We assessed the spatial and temporal invasion dynamics of both viruses to analyze transmission between cities using a suite of (i) gravity models, (ii) Stouffer’s rank models, and (iii) radiation models with two types of distance metrics, geographic distance and travel time between cities. Invasion risk was best captured by a gravity model when accounting for geographic distance and intermediate levels of density dependence; Stouffer’s rank model with geographic distance performed similarly well. Although a few long-distance invasion events occurred at the beginning of the epidemics, an estimated distance power of 1.7 (95% CrI: 1.5–2.0) from the gravity models suggests that spatial spread was primarily driven by short-distance transmission. Similarities between the epidemics were highlighted by jointly fitted models, which were preferred over individual models when the transmission intensity was allowed to vary across arboviruses. However, ZIKV spread considerably faster than CHIKV.


2019 ◽  
Author(s):  
Rebecca Kahn ◽  
Corey M. Peak ◽  
Juan Fernández-Gracia ◽  
Alexandra Hill ◽  
Amara Jambai ◽  
...  

AbstractForecasting the spatiotemporal spread of infectious diseases during an outbreak is an important component of epidemic response. However, it remains challenging both methodologically and with respect to data requirements as disease spread is influenced by numerous factors, including the pathogen’s underlying transmission parameters and epidemiological dynamics, social networks and population connectivity, and environmental conditions. Here, using data from Sierra Leone we analyze the spatiotemporal dynamics of recent cholera and Ebola outbreaks and compare and contrast the spread of these two pathogens in the same population. We develop a simulation model of the spatial spread of an epidemic in order to examine the impact of a pathogen’s incubation period on the dynamics of spread and the predictability of outbreaks. We find that differences in the incubation period alone can determine the limits of predictability for diseases with different natural history, both empirically and in our simulations. Our results show that diseases with longer incubation periods, such as Ebola, where infected individuals can travel further before becoming infectious, result in more long-distance sparking events and less predictable disease trajectories, as compared to the more predictable wave-like spread of diseases with shorter incubation periods, such as cholera.Significance statementUnderstanding how infectious diseases spread is critical for preventing and containing outbreaks. While advances have been made in forecasting epidemics, much is still unknown. Here we show that the incubation period – the time between exposure to a pathogen and onset of symptoms – is an important factor in predicting spatiotemporal spread of disease and provides one explanation for the different trajectories of the recent Ebola and cholera outbreaks in Sierra Leone. We find that outbreaks of pathogens with longer incubation periods, such as Ebola, tend to have less predictable spread, whereas pathogens with shorter incubation periods, such as cholera, spread in a more predictable, wavelike pattern. These findings have implications for the scale and timing of reactive interventions, such as vaccination campaigns.


2016 ◽  
Vol 41 (1) ◽  
pp. 103-128 ◽  
Author(s):  
Karima Kourtit ◽  
Peter Nijkamp

Creativity has in recent years received much attention from the research community, in relation to both technological innovation and knowledge spillovers. In the same vein, the concept of a creative class and of a creative city has gained a rising popularity. The present study aims to investigate the impacts of the urban “ambiance” on the spatial dispersion of heterogeneous types of creative people over different urban agglomerations. To that end, creative people are classified according to their profession or job class into Bohemians, creative core, and creative professionals. This article, then, seeks to relate the presence of each of these groups to the cultural ambiance of a given locality beside other moderator variables. Next, an econometric model is constructed and applied to explain the spatial distribution of creative professions in the Netherlands. Our study first maps out the spatial spread of these three creative classes in the Netherlands. Next, the shares of these creative classes are related to cultural, ecological, ethnic, and geographic characteristics of Dutch municipalities. Our results show that Bohemians and people belonging to the creative core exhibit a specific spatial pattern: they appear to be overrepresented in municipalities with a relative overconcentration of culture, nature, and ethnic diversity and with a short distance to job places.


2014 ◽  
Vol 112 (1) ◽  
pp. 172-177 ◽  
Author(s):  
Huaiyu Tian ◽  
Sen Zhou ◽  
Lu Dong ◽  
Thomas P. Van Boeckel ◽  
Yujun Cui ◽  
...  

The spatial spread of the highly pathogenic avian influenza virus H5N1 and its long-term persistence in Asia have resulted in avian influenza panzootics and enormous economic losses in the poultry sector. However, an understanding of the regional long-distance transmission and seasonal patterns of the virus is still lacking. In this study, we present a phylogeographic approach to reconstruct the viral migration network. We show that within each wild fowl migratory flyway, the timing of H5N1 outbreaks and viral migrations are closely associated, but little viral transmission was observed between the flyways. The bird migration network is shown to better reflect the observed viral gene sequence data than other networks and contributes to seasonal H5N1 epidemics in local regions and its large-scale transmission along flyways. These findings have potentially far-reaching consequences, improving our understanding of how bird migration drives the periodic reemergence of H5N1 in Asia.


Author(s):  
William E. Fitzgibbon ◽  
Jeffrey J. Morgan ◽  
Glenn F. Webb ◽  
Yixiang Wu

A mathematical model incorporating  diffusion is developed to describe the spatial spread of COVID-19 epidemics in geographical regions. The dynamics of the spatial spread are based on community transmission of the virus. The model is applied to the outbreak of the COVID-19 epidemic in Brazil.


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