Early Transmission Dynamics Research Articles

SARS-COV-2 outbreak and control in Kenya - Mathematical model analysis.

The coronavirus disease 2019 (COVID-19) pandemic reached Kenya in March 2020 with the initial cases reported in the capital city Nairobi and in the coastal area Mombasa. As reported by the World Health Organization, the outbreak of COVID-19 has spread across the world, killed many, collapsed economies and changed the way people live since it was first reported in Wuhan, China, in the end of 2019. As at the end of December 2020, it had led to over 2.8 million confirmed cases in Africa with over 67 thousand deaths. 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 SEIHCRD mathematical transmission model with reported Kenyan data on cases of COVID-19 to estimate how transmission varies over time. 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. The next generation matrix approach was adopted to calculate the basic reproduction number (R0) from the model to assess the factors driving the infection. The model illustrates the effect of mass testing on COVID-19 as well as individual self initiated behavioral change. The results have significant impact on the management of COVID-19 and implementation of prevention policies. The results from the model analysis shows that aggressive and effective mass testing as well as individual self initiated behaviour change play a big role in getting rid of the COVID-19 epidemic otherwise the rate of infection will continue to increase despite the increased rate of recovery.

Assessing the Effectiveness of Isolation and Contact-Tracing Interventions for Early Transmission Dynamics of COVID-19 in South Korea.

Recent COVID-19 outbreaks pose serious public health challenges all around the world. South Korea had experienced the early outbreak of the COVID-19 pandemic and implemented early effective interventions. The 2020 COVID-19 outbreak in South Korea showed spatial hot spots and super-spreading events. As a result of these super-spreading events, three huge outbreaks of the COVID-19 have occurred in Korea from February to December 2020. To capture the intrinsic nature of heterogeneity, an agent-based model has been developed focusing on early transmission dynamics of COVID-19 in South Korea. Based on the social empirical contact information of early confirmed cases of COVID-19, we have constructed a scale-free network. Our agent-based model has incorporated essential individual variability such as different contact numbers and infectivity levels. In the absence of vaccines or treatment, contact tracing, case-isolation, quarantine are the most critical interventions to prevent larger outbreaks. First, we investigate the impacts of critical factors on various epidemic outputs such as incidence and cumulative incidence. These critical factors include contact numbers, transmission rates, infectivity of presymptomatic or asymptomatic cases, and contact-tracing with quarantine intervention. Furthermore, the effectiveness of case isolation and contact-tracing (followed by quarantine) is evaluated under various scenarios. Our results indicate that case isolation combined with contact-tracing quarantine is much more effective under a moderate level of n}{}mathcal R_{0}n (smaller transmission rates or contact numbers) and presymptomatic cases. However, the efficacy of interventions reduces significantly for a higher level of n}{}mathcal R_{0}n (larger transmission rates or contact numbers) with a high level of infectivity (in presymptomatic cases). This highlights the key role of efficient contact-tracing and case-isolation to mitigate larger outbreaks or super-spreading events.

Early transmission of SARS-CoV-2 in South Africa: An epidemiological and phylogenetic report

ObjectivesThe Network for Genomic Surveillance in South Africa (NGS-SA) was formed to investigate the introduction and understand the early transmission dynamics of the SARS-CoV-2 epidemic in South-Africa. DesignThis paper presents the first results from this group, which is a molecular epidemiological study of the first 21 SARS-CoV-2 whole genomes sampled in the first port of entry – KwaZulu-Natal (KZN) – during the first month of the epidemic. By combining this with calculations of the effective reproduction number (R), it aimed to shed light on the patterns of infections in South Africa. ResultsTwo of the largest provinces – Gauteng and KZN – had a slow growth rate for the number of detected cases, while the epidemic spread faster in the Western Cape and Eastern Cape. The estimates of transmission potential suggested a decrease towards R = 1 since the first cases and deaths, but a subsequent estimated R average of 1.39 between 6–18 May 2020. It was also demonstrated that early transmission in KZN was associated with multiple international introductions and dominated by lineages B1 and B. Evidence for locally acquired infections in a hospital in Durban within the first month of the epidemic was also provided. ConclusionThe COVID-19 pandemic in South Africa was very heterogeneous in its spatial dimension, with many distinct introductions of SARS-CoV2 in KZN and evidence of nosocomial transmission, which inflated early mortality in KZN. The epidemic at the local level was still developing and NGS-SA aimed to clarify the dynamics in South Africa and devise the most effective measures as the outbreak evolved.

The role of case importation in explaining differences in early SARS-CoV-2 transmission dynamics in Canada—A mathematical modeling study of surveillance data

ObjectiveThe North American coronavirus disease-2019 (COVID-19) epidemic exhibited distinct early trajectories. In Canada, Quebec had the highest COVID-19 burden and its earlier March school break, taking place two weeks before those in other provinces, could have shaped early transmission dynamics. MethodsWe combined a semi-mechanistic model of SARS-CoV-2 transmission with detailed surveillance data from Quebec and Ontario (initially accounting for 85% of Canadian cases) to explore the impact of case importation and timing of control measures on cumulative hospitalizations. ResultsA total of 1544 and 1150 cases among returning travelers were laboratory-confirmed in Quebec and Ontario, respectively (symptoms onset ≤03-25-2020). Hospitalizations could have been reduced by 55% (95% CrI: 51%–59%) if no cases had been imported after Quebec’s March break. However, if Quebec had experienced Ontario’s number of introductions, hospitalizations would have only been reduced by 12% (95% CrI: 8%–16%). Early public health measures mitigated the epidemic spread as a one-week delay could have resulted in twice as many hospitalizations (95% CrI: 1.7–2.1). ConclusionBeyond introductions, factors such as public health preparedness, responses and capacity could play a role in explaining interprovincial differences. In a context where regions are considering lifting travel restrictions, coordinated strategies and proactive measures are to be considered.

Uncovering two phases of early intercontinental COVID-19 transmission dynamics.

Background: The COVID-19 pandemic has posed an ongoing global crisis, but how the virus spread across the world remains poorly understood. This is of vital importance for informing current and future pandemic response strategies.Methods: We performed two independent analyses, travel network-based epidemiological modelling and Bayesian phylogeographic inference, to investigate the intercontinental spread of COVID-19.Results: Both approaches revealed two distinct phases of COVID-19 spread by the end of March 2020. In the first phase, COVID-19 largely circulated in China during mid- to late January, 2020, and was interrupted by containment measures in China. In the second and predominant phase extending from late February to mid-March, unrestricted movements between countries outside of China facilitated intercontinental spread, with Europe as a major source. Phylogenetic analyses also revealed that the dominant strains circulating in the United States of America were introduced from Europe. However, stringent restrictions on international travel across the world since late March have substantially reduced intercontinental transmission.Conclusions: Our analyses highlight that heterogeneities in international travel have shaped the spatiotemporal characteristics of the pandemic. Unrestricted travel caused a large number of COVID-19 exportations from Europe to other continents between late February and mid-March, which facilitated the COVID-19 pandemic. Targeted restrictions on international travel from countries with widespread community transmission, together with improved capacity in testing, genetic sequencing and contact tracing, can inform timely strategies for mitigating and containing COVID-19 outbreaks post-lockdown.

Social markers of a pandemic: modeling the association between cultural norms and COVID-19 spread data

While cross-national differences of the epidemic curves of COVID-19 become evident, social markers of such variability are still unexplored. In order to investigate how certain social norms may underlie the heterogeneity of the spread of infections, global social data (including cultural values, indices of prosperity, and government effectiveness) and covariates (such as climate zone, economic indicator, and healthcare access and quality) of early transmission dynamics of COVID-19 were collected. Model-based clustering and random forest regression analysis were applied to identify distinct groups of societies and explore predictors of COVID-19 doubling time. Clustering revealed four groups: (1) reserved; (2) drifting; (3) assertive; and (4) compliant societies. Compliant societies from dry climate zones showed the highest doubling times in spite of increased population densities. Most relevant predictors of doubling time were population density, freedom of assembly and association, and agency, underlining the importance of social factors in the hetereogeneity of COVID-19 transmission rates. Our cluster typology might contribute to the explanation of cross-national variability in early transmission dynamics of highly infectious diseases.

Effect of a wet market on coronavirus disease (COVID-19) transmission dynamics in China, 2019–2020

ObjectivesThe novel coronavirus (SARS-CoV-2) originating from Wuhan spread rapidly throughout China. While its origin remains uncertain, accumulating evidence links a wet market with the early spread of SARS-CoV-2 in Wuhan. Similarly, the influence of the marketplace on the early transmission dynamics is yet to be investigated. MethodsUsing the daily series of COVID-19 incidence, stratified according to contact history with the market, we have conducted quantitative modeling analyses to estimate the reproduction numbers (R) for market-to-human and human-to-human transmission, the reporting probability, and the early effects of public health interventions. ResultsWe estimated R at 0.24 (95% CrI: 0.01–1.38) for market-to-human transmission and 2.37 (95% CrI: 2.08–2.71) for human-to-human transmission during the early spread in China (2019–2020). Moreover, we estimated that the reporting rate for cases stemming from market-to-human transmission was 2–34 fold higher than that for cases stemming from human-to-human transmission, suggesting that contact history with the wet market played a key role in identifying COVID-19 cases. ConclusionsOur R estimate tied to market-to-human transmission had substantial uncertainty, but it was significantly lower compared with the reproduction number driving human-to-human transmission. Our results also suggest that asymptomatic and subclinical infections constitute a substantial component of the COVID-19 morbidity burden.

Optimal policies for control of the novel coronavirus disease (COVID-19) outbreak.

Understanding the early transmission dynamics of diseases and estimating the effectiveness of control policies play inevitable roles in the prevention of epidemic diseases. To this end, this paper is concerned with the design of optimal control strategies for the novel coronavirus disease (COVID-19). A mathematical model of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission based on Wuhan's data is considered. To solve the problem effectively and efficiently, a multi-objective genetic algorithm is proposed to achieve high-quality schedules for various factors including contact rate and transition rate of symptomatic infected individuals to the quarantined infected class. By changing these factors, two optimal policies are successfully designed. This study has two main scientific contributions that are: (1) This is pioneer research that proposes policies regarding COVID-19, (2) This is also the first research that addresses COVID-19 and considers its economic consequences through a multi-objective evolutionary algorithm. Numerical simulations conspicuously demonstrate that by applying the proposed optimal policies, governments could find useful and practical ways for control of the disease.

Early Transmission Dynamics of Novel Coronavirus (COVID-19) in Nigeria.

On 31 December 2019, the World Health Organization (WHO) was notified of a novel coronavirus disease in China that was later named COVID-19. On 11 March 2020, the outbreak of COVID-19 was declared a pandemic. The first instance of the virus in Nigeria was documented on 27 February 2020. This study provides a preliminary epidemiological analysis of the first 45 days of COVID-19 outbreak in Nigeria. We estimated the early transmissibility via time-varying reproduction number based on the Bayesian method that incorporates uncertainty in the distribution of serial interval (time interval between symptoms onset in an infected individual and the infector), and adjusted for disease importation. By 11 April 2020, 318 confirmed cases and 10 deaths from COVID-19 have occurred in Nigeria. At day 45, the exponential growth rate was 0.07 (95% confidence interval (CI): 0.05–0.10) with a doubling time of 9.84 days (95% CI: 7.28–15.18). Separately for imported cases (travel-related) and local cases, the doubling time was 12.88 days and 2.86 days, respectively. Furthermore, we estimated the reproduction number for each day of the outbreak using a three-weekly window while adjusting for imported cases. The estimated reproduction number was 4.98 (95% CrI: 2.65–8.41) at day 22 (19 March 2020), peaking at 5.61 (95% credible interval (CrI): 3.83–7.88) at day 25 (22 March 2020). The median reproduction number over the study period was 2.71 and the latest value on 11 April 2020, was 1.42 (95% CrI: 1.26–1.58). These 45-day estimates suggested that cases of COVID-19 in Nigeria have been remarkably lower than expected and the preparedness to detect needs to be shifted to stop local transmission.

Coronavirus Disease 2019 (COVID-19) and Cardiovascular Disease: A Viewpoint on the Potential Influence of Angiotensin-Converting Enzyme Inhibitors/Angiotensin Receptor Blockers on Onset and Severity of Severe Acute Respiratory Syndrome Coronavirus 2 Infection.

Coronavirus Disease 2019 (COVID-19) and Cardiovascular Disease: A Viewpoint on the Potential Influence of Angiotensin-Converting Enzyme Inhibitors/Angiotensin Receptor Blockers on Onset and Severity of Severe Acute Respiratory Syndrome Coronavirus 2 Infection.

Early Transmission Dynamics in Wuhan, China, of Novel Coronavirus–Infected Pneumonia

BackgroundThe initial cases of novel coronavirus (2019-nCoV)–infected pneumonia (NCIP) occurred in Wuhan, Hubei Province, China, in December 2019 and January 2020. We analyzed data on the first 425 confirmed cases in Wuhan to determine the epidemiologic characteristics of NCIP.MethodsWe collected information on demographic characteristics, exposure history, and illness timelines of laboratory-confirmed cases of NCIP that had been reported by January 22, 2020. We described characteristics of the cases and estimated the key epidemiologic time-delay distributions. In the early period of exponential growth, we estimated the epidemic doubling time and the basic reproductive number.ResultsAmong the first 425 patients with confirmed NCIP, the median age was 59 years and 56% were male. The majority of cases (55%) with onset before January 1, 2020, were linked to the Huanan Seafood Wholesale Market, as compared with 8.6% of the subsequent cases. The mean incubation period was 5.2 days (95% confidence interval [CI], 4.1 to 7.0), with the 95th percentile of the distribution at 12.5 days. In its early stages, the epidemic doubled in size every 7.4 days. With a mean serial interval of 7.5 days (95% CI, 5.3 to 19), the basic reproductive number was estimated to be 2.2 (95% CI, 1.4 to 3.9).ConclusionsOn the basis of this information, there is evidence that human-to-human transmission has occurred among close contacts since the middle of December 2019. Considerable efforts to reduce transmission will be required to control outbreaks if similar dynamics apply elsewhere. Measures to prevent or reduce transmission should be implemented in populations at risk. (Funded by the Ministry of Science and Technology of China and others.)

Early dynamics of transmission and control of COVID-19: a mathematical modelling study

SummaryBackgroundAn outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to 95 333 confirmed cases as of March 5, 2020. 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. Combining a mathematical model of severe SARS-CoV-2 transmission with four datasets from within and outside Wuhan, we estimated how transmission in Wuhan varied between December, 2019, and February, 2020. We used these estimates to assess the potential for sustained human-to-human transmission to occur in locations outside Wuhan if cases were introduced.MethodsWe combined a stochastic transmission model with data on cases of coronavirus disease 2019 (COVID-19) in Wuhan and international cases that originated in Wuhan to estimate how transmission had varied over time during January, 2020, and February, 2020. Based on these estimates, we then calculated the probability that newly introduced cases might generate outbreaks in other areas. To estimate the early dynamics of transmission in Wuhan, we fitted a stochastic transmission dynamic model to multiple publicly available datasets on cases in Wuhan and internationally exported cases from Wuhan. The four datasets we fitted to were: daily number of new internationally exported cases (or lack thereof), by date of onset, as of Jan 26, 2020; daily number of new cases in Wuhan with no market exposure, by date of onset, between Dec 1, 2019, and Jan 1, 2020; daily number of new cases in China, by date of onset, between Dec 29, 2019, and Jan 23, 2020; and proportion of infected passengers on evacuation flights between Jan 29, 2020, and Feb 4, 2020. We used an additional two datasets for comparison with model outputs: daily number of new exported cases from Wuhan (or lack thereof) in countries with high connectivity to Wuhan (ie, top 20 most at-risk countries), by date of confirmation, as of Feb 10, 2020; and data on new confirmed cases reported in Wuhan between Jan 16, 2020, and Feb 11, 2020.FindingsWe estimated that the median daily reproduction number (Rt) in Wuhan declined from 2·35 (95% CI 1·15–4·77) 1 week before travel restrictions were introduced on Jan 23, 2020, to 1·05 (0·41–2·39) 1 week after. Based on our estimates of Rt, assuming SARS-like variation, we calculated that in locations with similar transmission potential to Wuhan in early January, once there are at least four independently introduced cases, there is a more than 50% chance the infection will establish within that population.InterpretationOur results show that COVID-19 transmission probably declined in Wuhan during late January, 2020, coinciding with the introduction of travel control measures. As more cases arrive in international locations with similar transmission potential to Wuhan before these control measures, it is likely many chains of transmission will fail to establish initially, but might lead to new outbreaks eventually.FundingWellcome Trust, Health Data Research UK, Bill & Melinda Gates Foundation, and National Institute for Health Research.

Physiotherapy in hospitalised patients with COVID-19 disease: what we know so far

International Journal of Therapy and RehabilitationVol. 27, No. 3 EditorialPhysiotherapy in hospitalised patients with COVID-19 disease: what we know so farMassimiliano PolastriMassimiliano PolastriCorrespondence to:E-mail Address: [email protected]Medical Department of Continuity of Care and Disability, Physical Medicine and Rehabilitation, St Orsola University Hospital, Bologna, ItalySearch for more papers by this authorMassimiliano PolastriPublished Online:10 Apr 2020https://doi.org/10.12968/ijtr.2020.0035AboutSectionsView articleView Full TextPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareShare onFacebookTwitterLinked InEmail View article References Ambrosino N, Makhabah DN. Comprehensive physiotherapy management in ARDS. Minerva Anestesiol. 2013;79(5):554–563 Google ScholarBBC News. Virus surges in US as death toll passes 1,000. https://www.bbc.co.uk/news/live/world-52044452 (accessed26 March 2020) Google ScholarChinese Association of Rehabilitation Medicine, Respiratory Rehabilitation Committee of Chinese Association of Rehabilitation Medicine, Cardiopulmonary Rehabilitation Group of Chinese Society of Physical Medicine and Rehabilitation. Recommendations for respiratory rehabilitation of COVID-19 in adults. Chinese Journal of Tuberculosis and Respiratory Diseases. 2020;43(0):E029. https://doi.org/10.3760/cma.j.cn112147-20200228-00206 Google ScholarEuropean Centre for Disease Prevention and Control. Situation update for the EU/EEA and the UK, as of 23 March 2020. 2020. https://www.ecdc.europa.eu/en/cases-2019-ncov-eueea (accessed25 March 2020) Google ScholarHui DS, Chow BK, Chu L et al.. Exhaled air dispersion and removal is influenced by isolation room size and ventilations settings during oxygen delivery via nasal cannula. Respirology. 2011;16(6):1005–1013. https://doi.org/10.1111/j.1440-1843.2011.01995.x Crossref, Google ScholarHui DS, Chow BK, Chu L et al.. Exhaled air dispersion during coughing with and without wearing a surgical or N95 mask. PLoS One. 2012;7(12):e50845. https://doi.org/10.1371/journal.pone.0050845 Crossref, Google ScholarHui DS, Chow BK, Lo T et al.. Exhaled air dispersion during non invasive ventilation via helmets and a total facemask. Chest. 2015;147(5):1336–1343. https://doi.org/10.1378/chest.14-1934 Crossref, Google ScholarLazzeri M, Lanza A, Bellini R et al.. Respiratory physiotherapy in patients with COVID-19 infection in acute setting: position paper of the Italian Association of Respiratory Physiotherapists (ARIR).. Monaldi Arch Chest Dis. 2020;90:1285. https://doi.org/10.408/monaldi.2020.1285 Crossref, Google ScholarLi Q, Guan X, Wu P et al.. Early transmission dynamics in Wuhan, China, of novel coronavirus-infected pneumonia. N Engl J Med. 2020. https://doi.org/10.1056/NEJMoa2001316 Crossref, Google ScholarSimonds AK, Hanak A, Chatwin M et al.. Evaluation of droplet dispersion during non-invasive ventilation, oxygen therapy, nebuliser treatment and chest physiotherapy in clinical practice: implications for management of pandemic influenza and other airborne infections. Health Technol Assess. 2010;14(46):131–172. https://doi.org/10.3310/hta14460-02 Crossref, Google ScholarWorld Health Organization. Coronavirus disease (COVID-2019) situation reports. 2020. https://www.who.int/docs/default-source/coronaviruse/situation-reports/20200322-sitrep-62-covid-19.pdf?sfvrsn=f7764c46_2 (accessed25 March 2020) Google ScholarYang F, Liu N, Wu JY et al.. Pulmonary rehabilitation guidelines in the principle of 4S for patients infected with 2019 novel coronavirus (2019-nCoV). Zhonghua Jie He He Hu Xi Za Zhi. 2020;43(0):E004. https://doi.org/10.3760/cma.j.issn.1001-0939.2020.0004 Google ScholarZhu N, Zhang D, Wang W et al.. A novel coronavirus from patients with pneumonia in China, 2019. N Engl J Med. 2020;382(8):727–733. https://doi.org/10.1056/NEJMoa2001017 Crossref, Google Scholar FiguresReferencesRelatedDetailsCited byCOVID-19: emerging challenges in maintaining physical function in patients who have had haematopoietic cell transplantsJaleel Mohammed, Anne Gonzales, Hadeel R Bakhsh, Jayanti Rai, Nnenna Chigbo, Shahrukh K Hashmi2 October 2020 | International Journal of Therapy and Rehabilitation, Vol. 27, No. 9Developing patient information and clinician education resources for venous thromboembolism in a physiotherapy department in response to the COVID-19 pandemicChris Drake, Nicola Hicks, Leanne Atkin2 October 2020 | International Journal of Therapy and Rehabilitation, Vol. 27, No. 9A new way forwardVicki Williams2 October 2020 | International Journal of Therapy and Rehabilitation, Vol. 27, No. 9 2 March 2020Volume 27Issue 3ISSN (online): 1759-779X Metrics History Published online 10 April 2020 Published in print 2 March 2020 Information© MA Healthcare LimitedPDF download

Early transmission dynamics of COVID-19 in a southern hemisphere setting: Lima-Peru: February 29th-March 30th, 2020.

The COVID-19 pandemic that emerged in Wuhan China has generated substantial morbidity and mortality impact around the world during the last four months. The daily trend in reported cases has been rapidly rising in Latin America since March 2020 with the great majority of the cases reported in Brazil followed by Peru as of April 15th, 2020. Although Peru implemented a range of social distancing measures soon after the confirmation of its first case on March 6th, 2020, the daily number of new COVID-19 cases continues to accumulate in this country. We assessed the early COVID-19 transmission dynamics and the effect of social distancing interventions in Lima, Peru.We estimated the reproduction number, R, during the early transmission phase in Lima from the daily series of imported and autochthonous cases by the date of symptoms onset as of March 30th, 2020. We also assessed the effect of social distancing interventions in Lima by generating short-term forecasts grounded on the early transmission dynamics before interventions were put in place.Prior to the implementation of the social distancing measures in Lima, the local incidence curve by the date of symptoms onset displays near exponential growth dynamics with the mean scaling of growth parameter, p, estimated at 0.96 (95% CI: 0.87, 1.0) and the reproduction number at 2.3 (95% CI: 2.0, 2.5). Our analysis indicates that school closures and other social distancing interventions have helped slow down the spread of the novel coronavirus, with the nearly exponential growth trend shifting to an approximately linear growth trend soon after the broad scale social distancing interventions were put in place by the government.While the interventions appear to have slowed the transmission rate in Lima, the number of new COVID-19 cases continue to accumulate, highlighting the need to strengthen social distancing and active case finding efforts to mitigate disease transmission in the region.

Impact of nonpharmacological interventions on COVID-19 transmission dynamics in India.

As of May 4, 2020, India has reported 42,836 confirmed cases and 1,389 deaths from COVID-19. India's multipronged response included nonpharmacological interventions (NPIs) like intensive case-based surveillance, expanding testing capacity, social distancing, health promotion, and progressive travel restrictions leading to a complete halt of international and domestic movements (lockdown). We studied the impact of NPI on transmission dynamics of COVID-19 epidemic in India and estimated the minimum level of herd immunity required to halt it. We plotted time distribution, estimated basic (R0) and time-dependent effective (Rt) reproduction numbers using software R, and calculated doubling time, the growth rate for confirmed cases from January 30 to May 4, 2020. Herd immunity was estimated using the latest Rtvalue. Time distribution showed a propagated epidemic with subexponential growth. Average growth rate, 21% in the beginning, reduced to 6% after an extended lockdown (May 3). Based on early transmission dynamics, R0was 2.38 (95% confidence interval [CI] =1.79-3.07). Early, unmitigated Rt= 2.51 (95% CI = 2.05-3.14) (March 15) reduced to 1.28 (95% CI = 1.22-1.32) and was 1.83 (95% CI = 1.71-1.93) at the end of lockdown Phase 1 (April 14) and 2 (May 3), respectively. Similarly, average early doubling time (4.3 days) (standard deviation [SD] = 1.86) increased to 5.4 days (SD = 1.03) and 10.9 days (SD = 2.19). Estimated minimum 621 million recoveries are required to halt COVID-19 spread if Rtremains below 2. India's early response, especially stringent lockdown, has slowed COVID-19 epidemic. Increased testing, intensive case-based surveillance and containment efforts, modulated movement restrictions while protecting the vulnerable population, and continuous monitoring of transmission dynamics should be a way forward in the absence of effective treatment, vaccine, and undetermined postinfection immunity.

Molecular epidemiology of HIV-1 in Iceland: Early introductions, transmission dynamics and recent outbreaks among injection drug users

The molecular epidemiology of HIV-1 in Iceland has not been described so far. Detailed analyses of the dynamics of HIV-1 can give insights for prevention of virus spread. The objective of the current study was to characterize the genetic diversity and transmission dynamics of HIV-1 in Iceland. Partial HIV-1 pol (1020bp) sequences were generated from 230 Icelandic samples, representing 77% of all HIV-1 infected individuals reported in the country 1985–2012. Maximum likelihood phylogenies were reconstructed for subtype/CRF assignment and determination of transmission clusters. Timing and demographic growth patterns were determined in BEAST. HIV-1 infection in Iceland was dominated by subtype B (63%, n=145) followed by subtype C (10%, n=23), CRF01_AE (10%, n=22), sub-subtype A1 (7%, n=15) and CRF02_AG (7%, n=15). Trend analysis showed an increase in non-B subtypes/CRFs in Iceland over the study period (p=0.003). The highest proportion of phylogenetic clustering was found among injection drug users (IDUs; 89%), followed by heterosexuals (70%) and men who have sex with men (35%). The time to the most recent common ancestor of the oldest subtype B cluster dated back to 1978 (median estimate, 95% highest posterior density interval: 1974–1981) suggesting an early introduction of HIV-1 into Iceland. A previously reported increase in HIV-1 incidence among IDUs 2009–2011 was revealed to be due to two separate outbreaks. Our study showed that a variety of HIV-1 subtypes and CRFs were prevalent in Iceland 1985–2012, with subtype B being the dominant form both in terms of prevalence and domestic spread. The rapid increase of HIV-1 infections among IDUs following a major economic crisis in Iceland raises questions about casual associations between economic factors, drug use and public health.

Letter to the editor: Early transmission dynamics of Ebola virus disease (EVD), West Africa, March to August 2014 – Eurosurveillance 17 September 2014

Letter to the editor: Early transmission dynamics of Ebola virus disease (EVD), West Africa, March to August 2014 – Eurosurveillance 17 September 2014

Early transmission dynamics of Ebola virus disease (EVD), West Africa, March to August 2014.

The effective reproduction number, Rt, of Ebola virus disease was estimated using country-specific data reported from Guinea, Liberia and Sierra Leone to the World Health Organization from March to August, 2014. Rt for the three countries lies consistently above 1.0 since June 2014. Country-specific Rt for Liberia and Sierra Leone have lied between 1.0 and 2.0. Rt<2 indicate that control could be attained by preventing over half of the secondary transmissions per primary case.

The Early Transmission Dynamics of H1N1pdm Influenza in the United Kingdom

We analyzed data on all laboratory-confirmed cases of H1N1pdm influenza in the UK to 10th June 2009 to estimate epidemiological characteristics. We estimated a mean incubation period of 2.05 days and serial interval of 2.5 days with infectivity peaking close to onset of symptoms. Transmission was initially sporadic but increased from mid-May in England and from early June in Scotland. We estimated 37% of transmission occurred in schools, 24% in households, 28% through travel abroad and the remainder in the wider community. Children under 16 were more susceptible to infection in the household (adjusted OR 5.80, 95% CI 2.99-11.82). Treatment with oseltamivir plus widespread use of prophylaxis significantly reduced transmission (estimated reduction 16%). Households not receiving oseltamivir within 3 days of symptom onset in the index case had significantly increased secondary attack rates (adjusted OR 3.42, 95% CI 1.51-8.55).