Early Transmission Dynamics Research Articles

Surveillance of Parrot Bornavirus in Taiwan Captive Psittaciformes

Parrot bornavirus (PaBV) is an infectious disease linked with proventricular dilatation disease (PDD) with severe digestive and neurological symptoms affecting psittacine birds. Despite its detection in 2008, PaBV prevalence in Taiwan remains unexplored. Taiwan is one of the leading psittacine bird breeders; hence, understanding the distribution of PaBV aids preventive measures in controlling spread, early disease recognition, epidemiology, and transmission dynamics. Here, we aimed to detect the prevalence rate of PaBV and assess its genetic variation in Taiwan. Among 124 psittacine birds tested, fifty-seven were PaBV-positive, a prevalence rate of 45.97%. Most of the PaBV infections were adult psittacine birds, with five birds surviving the infection, resulting in a low survival rate (8.77%). A year of parrot bornavirus surveillance presented a seasonal pattern, with peak PaBV infection rates occurring in the spring season (68%) and the least in the summer season (25%), indicating the occurrence of PaBV infections linked to seasonal factors. Histopathology reveals severe meningoencephalitis in the cerebellum and dilated cardiomyopathy of the heart in psittacine birds who suffered from PDD. Three brain samples underwent X/P gene sequencing, revealing PaBV-2 and PaBV-4 viral genotypes through phylogenetic analyses. This underscores the necessity for ongoing PaBV surveillance and further investigation into its pathophysiology and transmission routes.

Viral genetic clustering and transmission dynamics of the 2022 mpox outbreak in Portugal

Pathogen genome sequencing during epidemics enhances our ability to identify and understand suspected clusters and investigate their relationships. Here, we combine genomic and epidemiological data of the 2022 mpox outbreak to better understand early viral spread, diversification and transmission dynamics. By sequencing 52% of the confirmed cases in Portugal, we identified the mpox virus sublineages with the highest impact on case numbers and fitted them into a global context, finding evidence that several international sublineages probably emerged or spread early in Portugal. We estimated a 62% infection reporting rate and that 1.3% of the population of men who have sex with men in Portugal were infected. We infer the critical role played by sexual networks and superspreader gatherings, such as sauna attendance, in the dissemination of mpox virus. Overall, our findings highlight genomic epidemiology as a tool for the real-time monitoring and control of mpox epidemics, and can guide future vaccine policy in a highly susceptible population.

Tracking the incidence and risk factors for SARS-CoV-2 infection using historical maternal booking serum samples.

The early transmission dynamics of SARS-CoV-2 in the UK are unknown but their investigation is critical to aid future pandemic planning. We tested over 11,000 anonymised, stored historic antenatal serum samples, given at two north-west London NHS trusts in 2019 and 2020, for total antibody to SARS-CoV-2 receptor binding domain (anti-RBD). Estimated prevalence of seroreactivity increased from 1% prior to mid-February 2020 to 17% in September 2020. Our results show higher prevalence of seroreactivity to SARS-CoV-2 in younger, non-white ethnicity, and more deprived groups. We found no significant interaction between the effects of ethnicity and deprivation. Derived from prevalence, the estimated incidence of seroreactivity reflects the trends observed in daily hospitalisations and deaths in London that followed 10 and 13 days later, respectively. We quantified community transmission of SARS-CoV-2 in London, which peaked in late March / early April 2020 with no evidence of community transmission until after January 2020. Our study was not able to determine the date of introduction of the SARS-CoV-2 virus but demonstrates the value of stored antenatal serum samples as a resource for serosurveillance during future outbreaks.

COVID-19 Pandemic and Its Early Transmission Dynamics in Nigeria

The novel coronavirus COVID-19 originally identified in December 2019, based on the data issued by March 30, 2020 daily report, the epidemic of SARS-CoV-2 so far has caused 693224 cases and resulted in 33106 deaths in more than 200 countries. Referring to the data reported, World Health Organization declared the outbreak a pandemic. Several Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2 or COVID-19), showed high transmission capacity and morbidity. In this way, WHO suggests that the most efficient method for controlling transmission is social isolation/quarantine to the population. New cases of the pandemic arose everyday but Nigeria recorded its first death in 23rd March, 2020. The study used a secondary data culled from Worldometer from March 23, 2020 – May 5, 2020. The study examined the polynomial relationship between day and new cases of coronavirus in Nigeria and also check whether new cases will be predicted. Though, linear regression model was fitted to check the model that best fit the data. Result showedthat days had a positive effect on new cases and was found significant at P-value < 0.01 (1% significance level) for both linear and non-linear regression model. It was also revealed that nonlinear regression model best fit the model with an improved adjusted R square. It was established that there is a statistically significant relationship between days and new cases.However, government should take proper measures to reduce the spread of coronavirus and law enforcement the measures should be put into consideration. Keywords: Coronavirus, relationship, New cases, New death, Model DOI: 10.7176/RHSS/12-9-02 Publication date: May 31 st 2022

Extreme COVID-19 waves reveal hyperexponential growth and finite-time singularity

Coronavirus disease 2019 (COVID-19) has rapidly spread throughout our planet, bringing human lives to a standstill. Understanding the early transmission dynamics of a wave helps plan intervention strategies such as lockdowns that mitigate further spread, minimizing the adverse impact on humanity and the economy. Exponential growth of infections was thought to be the defining feature of an epidemic in its initial growth phase. Here we show that, contrary to common belief, early stages of extreme COVID-19 waves have an unbounded growth and finite-time singularity accompanying a hyperexponential power-law. The faster than exponential growth phase is hazardous and would entail stricter regulations to minimize further spread. Such a power-law description allows us to characterize COVID-19 waves better using single power-law exponents, rather than using piecewise exponentials. Furthermore, we identify the presence of log-periodic patterns decorating the power-law growth. These log-periodic oscillations may enable better prediction of the finite-time singularity. We anticipate that our findings of hyperexponential growth and log-periodicity will enable accurate modeling of outbreaks of COVID-19 or similar future outbreaks of other emergent epidemics.

The Effects of Race/Ethnicity, Age, and Area Deprivation Index (ADI) on COVID-19 Disease Early Dynamics: Washington, D.C. Case Study.

The COVID-19 pandemic and its associated mitigation strategies have significant psychosocial, behavioral, socioeconomic, and health impacts, particularly in vulnerable US populations. Different factors have been identified as influencers of the transmission rate; however, the effects of area deprivation index (as a measure of social determinants of health, SDoH) as a factor on COVID-19 disease early dynamics have not been established. We determined the effects of area deprivation index (ADI) and demographic factors on COVID-19 outcomes in Washington, D.C. This retrospective study used publicly available data on COVID-19 cases and mortality of Washington, D.C., during March 31st–July 4th, 2020. The main predictors included area deprivation index (ADI), age, and race/ethnicity. The ADI of each census block groups in D.C. (n=433) were obtained from Neighborhood Atlas map. Using a machine learning-based algorithm, the outcome variables were partitioned into time intervals: time duration (Pi, days), rate of change coefficient (Ei), and time segment load (Pi×Ei) for transmission rate and mortality. Correlation analysis and multiple linear regression models were used to determine associations between predictors and outcome variables. COVID-19 early transmission rate (E1) was highly correlated with ADI (SDoH; r= 0.88, p=0.0044) of the Washington, D.C. community. We also found positive association between ADI, age (0–17 years, r=0.91, p=0.0019), and race (African American/Black, r=0.86; p=0.0068) and COVID-19 outcomes. There was high variability in early transmission across the geographic regions (i.e., wards) of Washington, D.C., and this variability was driven by race/ethnic composition and ADI. Understanding the association of COVID-19 disease early transmission and mortality dynamics and key socio-demographic risk factors such as age, race, and ADI, as a measure of social determinants, will contribute to health equity/equality and distribution of economic resources/assistance and is essential for future predictive modeling of the COVID-19 pandemic to limit morbidity and mortality.Supplementary InformationThe online version contains supplementary material available at 10.1007/s40615-022-01238-1.

Rapid epidemic expansion of the SARS-CoV-2 Omicron variant in southern Africa

The SARS-CoV-2 epidemic in southern Africa has been characterized by three distinct waves. The first was associated with a mix of SARS-CoV-2 lineages, while the second and third waves were driven by the Beta (B.1.351) and Delta (B.1.617.2) variants, respectively1,2,3. In November 2021, genomic surveillance teams in South Africa and Botswana detected a new SARS-CoV-2 variant associated with a rapid resurgence of infections in Gauteng province, South Africa. Within three days of the first genome being uploaded, it was designated a variant of concern (Omicron, B.1.1.529) by the World Health Organization and, within three weeks, had been identified in 87 countries. The Omicron variant is exceptional for carrying over 30 mutations in the spike glycoprotein, which are predicted to influence antibody neutralization and spike function4. Here we describe the genomic profile and early transmission dynamics of Omicron, highlighting the rapid spread in regions with high levels of population immunity.

Rapid epidemic expansion of the SARS-CoV-2 Omicron variant in southern Africa

The SARS-CoV-2 epidemic in southern Africa has been characterized by three distinct waves. The first was associated with a mix of SARS-CoV-2 lineages, while the second and third waves were driven by the Beta (B.1.351) and Delta (B.1.617.2) variants, respectively1–3. In November 2021, genomic surveillance teams in South Africa and Botswana detected a new SARS-CoV-2 variant associated with a rapid resurgence of infections in Gauteng province, South Africa. Within three days of the first genome being uploaded, it was designated a variant of concern (Omicron, B.1.1.529) by the World Health Organization and, within three weeks, had been identified in 87 countries. The Omicron variant is exceptional for carrying over 30 mutations in the spike glycoprotein, which are predicted to influence antibody neutralization and spike function4. Here we describe the genomic profile and early transmission dynamics of Omicron, highlighting the rapid spread in regions with high levels of population immunity.

The serial and generation intervals from SARS-CoV-2 transmission dynamics and their potential application in the epidemiology of two citrus diseases

<p>Since the start of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causing the coronavirus disease 2019 (COVID-19) pandemic, the concepts of serial and generation intervals have been used as key epidemiological measures to understand the transmission dynamics of the disease. We carefully examined and repurposed these concepts to the understanding of the transmission chain and dynamics of two major citrus diseases: tristeza virus (caused by Citrus tristeza virus, CTV) and Huanglongbing (caused by <em>Candidatus</em> Liberibacter asiaticus). Following the fundamental definition of the concepts, the review delineates the transmission chain in the SARS-CoV-2 and that of CTV and CLas, pointing out their major similarities and differences. Then, it discusses estimation of the serial and generation intervals and their distributions for both plant diseases. Identification of infector-infectee tree pairs in a transmission chain within orchards is proposed through use of disease incidence data from intensive mapping, spatial pattern analysis, conditional probability, and simulation approaches. Like in SARS-CoV-2 dynamics, pre-symptomatic transmission in these two plant pathosystems is of epidemiological significance. Hence, estimation of the serial and generation interval can lay the foundations to understanding of early disease transmission dynamics, thus the implementation of vector control measures or eradication of infected trees. We hope this review motivates discussions on estimation and usage of these concepts to enhance understanding of the epidemiology of both of the herein examined citrus diseases.</p>

Early Spatiotemporal Patterns and Population Characteristics of the COVID-19 Pandemic in Southeast Asia

This observational study aims to investigate the early disease patterns of coronavirus disease 2019 (COVID-19) in Southeast Asia, consequently providing historical experience for further interventions. Data were extracted from official websites of the WHO and health authorities of relevant countries. A total of 1346 confirmed cases of COVID-19, with 217 recoveries and 18 deaths, were reported in Southeast Asia as of 16 March 2020. The basic reproductive number (R0) of COVID-19 in the region was estimated as 2.51 (95% CI:2.31 to 2.73), and there were significant geographical variations at the subregional level. Early transmission dynamics were examined with an exponential regression model: y = 0.30e0.13x (p < 0.01, R2 = 0.96), which could help predict short-term incidence. Country-level disease burden was positively correlated with Human Development Index (r = 0.86, p < 0.01). A potential early shift in spatial diffusion patterns and a spatiotemporal cluster occurring in Malaysia and Singapore were detected. Demographic analyses of 925 confirmed cases indicated a median age of 44 years and a sex ratio (male/female) of 1.25. Age may play a significant role in both susceptibilities and outcomes. The COVID-19 situation in Southeast Asia is challenging and unevenly geographically distributed. Hence, enhanced real-time surveillance and more efficient resource allocation are urgently needed.

Early transmission dynamics of SARS-CoV-2 in Indonesia

The objective of this study was to determine the transmission dynamics of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and to evaluate the vigilance of the health system during the early phase of coronavirus disease 2019 (COVID-19) outbreak in Indonesia. The early epidemiology and transmission chains of COVID-19 were analyzed based on data from the Directorate General of Disease Prevention and Control of the Indonesian Ministry of Health. The results of this study shown although Indonesia is a country with a high relative importation risk of SARS-CoV-2, the first two cases of COVID-19 were identified on March 2, 2020. This relatively late date by regional standards raises the possibility of undetected cases beforehand. The first case was a foreigner citizen who visited the capital city of Jakarta and later was diagnosed COVID-19 after returning from Indonesia. One week later after the first case, 27 confirmed COVID-19 cases had been reported in Indonesia, and the majority of the cases were clustered together. Apart from the possibility of underdetection of COVID-19 cases in the country, the government has strengthened the disease surveillance system and established an outbreak preparedness system to diagnose and control COVID-19.

Genomic epidemiology of SARS-CoV-2 transmission lineages in Ecuador.

Characterisation of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) genetic diversity through space and time can reveal trends in virus importation and domestic circulation and permit the exploration of questions regarding the early transmission dynamics. Here, we present a detailed description of SARS-CoV-2 genomic epidemiology in Ecuador, one of the hardest hit countries during the early stages of the coronavirus-19 pandemic. We generated and analysed 160 whole genome sequences sampled from all provinces of Ecuador in 2020. Molecular clock and phylogeographic analysis of these sequences in the context of global SARS-CoV-2 diversity enable us to identify and characterise individual transmission lineages within Ecuador, explore their spatiotemporal distributions, and consider their introduction and domestic circulation. Our results reveal a pattern of multiple international importations across the country, with apparent differences between key provinces. Transmission lineages were mostly introduced before the implementation of non-pharmaceutical interventions, with differential degrees of persistence and national dissemination.

Preliminary Evidence from a Cross-sectional Study on Epidemiology and Early Transmission Dynamics of COVID-19 in Karnataka State of India

Abstract Introduction Coronavirus disease 2019 (COVID-19) is an emerging infection and quickly disseminated around the world. This article studies the epidemiology and early transmission dynamics of COVID-19 in Karnataka, which would be useful for effective epidemic management and policy formulation. Materials and Methods All COVID-19 cases reported in the state of Karnataka, India, till June 12, 2020, are included in the study. The epidemiology and transmission dynamics of COVID-19 in Karnataka is studied through descriptive statistical analysis. Results The findings illustrate a gender-, age-, and region-based disparity in the susceptibility and fatality. There appears to be a male preponderance in the susceptibility, but a female preponderance in fatality. It is also found that the adults are more susceptible to the infection, while the elderly have the risk of high fatality. Further, infected individuals in the region with urbanization have a higher risk of fatality than other regions. The study shows that the chances of recovery for females are lower than males, and further, the chances of recovery are positively related to the age of the infected person. The chances of recovery are higher if the infected individual is younger and they diminish if the individual is older. The study also explores that the chances of recovery are affected by the patient’s geographical location. It is also noted that individuals who returned from foreign travel have better chances of recovery than the locally transmitted individuals. Conclusion Though the risk of susceptibility to COVID-19 infection is equal to all, the burden of getting infected and the burden of fatality is unequally distributed among different demographic categories. To manage the contagious spread of epidemic, to reduce fatality, and to increase the chances of recovery, targeted policy actions are suggested to benefit the vulnerable demographic categories.

Estimating effects of intervention measures on COVID-19 outbreak in Wuhan taking account of improving diagnostic capabilities using a modelling approach

BackgroundAlthough by late February 2020 the COVID-19 epidemic was effectively controlled in Wuhan, China, estimating the effects of interventions, such as transportation restrictions and quarantine measures, on the early COVID-19 transmission dynamics in Wuhan is critical for guiding future virus containment strategies. Since the exact number of infected cases is unknown, the number of documented cases was used by many disease transmission models to infer epidemiological parameters. This means that it was possible to produce biased estimates of epidemiological parameters and hence of the effects of intervention measures, because the percentage of all cases that were documented changed during the first 2 months of the epidemic, as a consequence of a gradually improving diagnostic capability.MethodsTo overcome these limitations, we constructed a stochastic susceptible-exposed-infected-quarantined-recovered (SEIQR) model, accounting for intervention measures and temporal changes in the proportion of new documented infections out of total new infections, to characterize the transmission dynamics of COVID-19 in Wuhan across different stages of the outbreak. Pre-symptomatic transmission was taken into account in our model, and all epidemiological parameters were estimated using the Particle Markov-chain Monte Carlo (PMCMC) method.ResultsOur model captured the local Wuhan epidemic pattern as two-peak transmission dynamics, with one peak on February 4 and the other on February 12, 2020. The impact of intervention measures determined the timing of the first peak, leading to an 86% drop in the Re from 3.23 (95% CI, 2.22 to 4.20) to 0.45 (95% CI, 0.20 to 0.69). The improved diagnostic capability led to the second peak and a higher proportion of documented infections. Our estimated proportion of new documented infections out of the total new infections increased from 11% (95% CI 1–43%) to 28% (95% CI 4–62%) after January 26 when more detection kits were released. After the introduction of a new diagnostic criterion (case definition) on February 12, a higher proportion of daily infected cases were documented (49% (95% CI 7–79%)).ConclusionsTransportation restrictions and quarantine measures together in Wuhan were able to contain local epidemic growth.

COVID-19 (SARS-CoV-2) Outbreaks in Rural United Kingdom: Patterns of Spread and Empirical Evidence for the 'Rule of Six'

Background: Multiple countries across the world are facing the second wave of severe acute respiratory syndrome coronavirus. Understanding the early transmission dynamics is crucial for outbreak control measures and evaluating public health policy. Methods: We used detailed contact tracing data for the rural county of Lincolnshire to quantify risk factors of infection and model disease spread. We used subcritical branching processes ( Findings: 158,390 individuals were contact traced between the periods of May 11, 2020 to September 14, 2020 during the early phase of the second wave with 1143 confirmed positives in Lincolnshire. We found empirical evidence in support of the “rule of 6” for secondary contacts and largest gathering component size of 60 people. There is highly-over dispersed offspring distribution k=0·35 95% CI [0·41-0·29] indicative of super spreading events. We found through multivariable analysis, evidence of young adult population driving the outbreak and protection increasing with age for those in isolation. There is an increased risk of 66% (IRR 1·66, 95% CI [1·02 - 2·20]), 50% (IRR 1·50, 95% CI [1·16 - 2·37]) associated with eating out and shopping followed by social events respectively when compared to house hold acquired covid-19 infections. Interpretation: Generating functions and random graph theory help model the local transmission heterogeneities from the very onset of the outbreak. Focusing on the secondary contacts and case cluster size distribution is a better method for detecting the stochastic beginnings of a pandemic, rather than decision-making being based on exceeding particular threshold. Contact tracing and viral testing is essential to provide the reliable data and help with the public health policy for effective interventions. Funding: NoneDeclaration of Interests: None.Ethics Approval Statement: Ethics permission has been granted by University of Lincoln Ethics Committee (Protocol ID: 4188).

Evidence of the effectiveness of travel-related measures during the early phase of the COVID-19 pandemic: a rapid systematic review

ObjectiveTo review the effectiveness of travel measures implemented during the early stages of the COVID-19 pandemic to inform changes on how evidence is incorporated in the International Health Regulations (2005)...

Early Transmission Dynamics, Spread, and Genomic Characterization of SARS-CoV-2 in Panama.

We report an epidemiologic analysis of 4,210 cases of infection with severe acute respiratory syndrome coronavirus 2 and genetic analysis of 313 new near-complete virus genomes in Panama during March 9–April 16, 2020. Although containment measures reduced R0 and Rt, they did not interrupt virus spread in the country.

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

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.

COVID-19 outbreak, social distancing and mass testing in Kenya-insights from a mathematical model

As reported by the World Health Organization (WHO), the world is currently facing a devastating pandemic of a novel coronavirus (COVID-19), which started as an outbreak of pneumonia of unknown cause in the Wuhan city of China in December 2019. Since then, the respiratory disease has exponentially spread to over 210 countries. By the end of April, COVID-19 had caused over three million confirmed cases of infections and over 200,000 fatalities globally. The trend poses a huge threat to global public health. Understanding the early transmission dynamics of the infection and evaluating the effectiveness of control measures is crucial for assessing the potential for sustained transmission to occur in new areas. We employed a SEIHQRD delay differential mathematical transmission model with reported Kenyan data on cases of COVID-19 to estimate how transmission varies over time and which population to target for mass testing. The model is concise in structure, and successfully captures the course of the COVID-19 outbreak, and thus sheds light on understanding the trends of the outbreak and the vulnerable populations. The results show that, the government should target population in the informal settlement for mass testing and provide affordable sanitizers and clean water to this population. The model results also indicate that people with pre-existing non-communicable diseases (NCDs) should be identified and given special medical care. Given the absence of vaccine at the moment, non-pharmaceutical intervention is needed to effectively reduce the final epidemic size.

Analysis of intervention effectiveness using early outbreak transmission dynamics to guide future pandemic management and decision-making in Kuwait.

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is a World Health Organization designated pandemic that can result in severe symptoms and death that disproportionately affects older patients or those with comorbidities. Kuwait reported its first imported cases of COVID-19 on February 24, 2020. Analysis of data from the first three months of community transmission of the COVID-19 outbreak in Kuwait can provide important guidance for decision-making when dealing with future SARS-CoV-2 epidemic wave management. The analysis of intervention scenarios can help to evaluate the possible impacts of various outbreak control measures going forward which aim to reduce the effective reproduction number during the initial outbreak wave. Herein we use a modified susceptible-exposed-asymptomatic-infectious-removed (SEAIR) transmission model to estimate the outbreak dynamics of SARS-CoV-2 transmission in Kuwait. We fit case data from the first 96 days in the model to estimate the effective reproduction number and used Google mobility data to refine community contact matrices. The SEAIR modelled scenarios allow for the analysis of various interventions to determine their effectiveness. The model can help inform future pandemic wave management, not only in Kuwait but for other countries as well.