Estimates Of R0 Research Articles

A Conceptual Discussion About the Basic Reproduction Number of Severe Acute Respiratory Syndrome Coronavirus 2 in Healthcare Settings

To date, no specific estimate of R0 for SARS-CoV-2 is available for healthcare settings. Using interindividual contact data, we highlight that R0 estimates from the community cannot translate directly to healthcare settings, with pre-pandemic R0 values ranging 1.3–7.7 in 3 illustrative healthcare institutions. This has implications for nosocomial COVID-19 control.

Assessing the impact of non-pharmaceutical interventions on SARS-CoV-2 transmission in Switzerland.

Following the rapid dissemination of COVID-19 cases in Switzerland, large-scale non-pharmaceutical interventions (NPIs) were implemented by the cantons and the federal government between 28 February and 20 March 2020. Estimates of the impact of these interventions on SARS-CoV-2 transmission are critical for decision making in this and future outbreaks. We here aim to assess the impact of these NPIs on disease transmission by estimating changes in the basic reproduction number (R0) at national and cantonal levels in relation to the timing of these NPIs. We estimated the time-varying R0 nationally and in eleven cantons by fitting a stochastic transmission model explicitly simulating within-hospital dynamics. We used individual-level data from more than 1000 hospitalised patients in Switzerland and public daily reports of hospitalisations and deaths. We estimated the national R0 to be 2.8 (95% confidence interval 2.1–3.8) at the beginning of the epidemic. Starting from around 7 March, we found a strong reduction in time-varying R0 with a 86% median decrease (95% quantile range [QR] 79–90%) to a value of 0.40 (95% QR 0.3–0.58) in the period of 29 March to 5 April. At the cantonal level, R0 decreased over the course of the epidemic between 53% and 92%. Reductions in time-varying R0 were synchronous with changes in mobility patterns as estimated through smartphone activity, which started before the official implementation of NPIs. We inferred that most of the reduction of transmission is attributable to behavioural changes as opposed to natural immunity, the latter accounting for only about 4% of the total reduction in effective transmission. As Switzerland considers relaxing some of the restrictions of social mixing, current estimates of time-varying R0 well below one are promising. However, as of 24 April 2020, at least 96% (95% QR 95.7–96.4%) of the Swiss population remains susceptible to SARS-CoV-2. These results warrant a cautious relaxation of social distance practices and close monitoring of changes in both the basic and effective reproduction numbers.

COVID-19 UK Lockdown Forecasts and R 0.

Introduction: The first reported UK case of COVID-19 occurred on 30 January 2020. A lockdown from 24 March was partially relaxed on 10 May. One model to forecast disease spread depends on clinical parameters and transmission rates. Output includes the basic reproduction number R0 and the log growth rate r in the exponential phase.Methods: Office for National Statistics data on deaths in England and Wales is used to estimate r. A likelihood for the transmission parameters is defined from a gaussian density for r using the mean and standard error of the estimate. Parameter samples from the Metropolis-Hastings algorithm lead to an estimate and credible interval for R0 and forecasts for cases and deaths.Results: The UK initial log growth rate is r = 0.254 with s.e. 0.004. R0 = 6.94 with 95% CI (6.52, 7.39). In a 12 week lockdown from 24 March with transmission parameters reduced throughout to 5% of their previous values, peaks of around 90,000 severely and 25,000 critically ill patients, and 44,000 cumulative deaths are expected by 16 June. With transmission rising from 5% in mid-April to reach 30%, 50,000 deaths and 475,000 active cases are expected in mid-June. Had such a lockdown begun on 17 March, around 30,000 (28,000, 32,000) fewer cumulative deaths would be expected by 9 June.Discussion: The R0 estimate is compatible with some international estimates but over twice the value quoted by the UK government. An earlier lockdown could have saved many thousands of lives.

Cascade Reaction-Based Chemiresistive Array for Ethylene Sensing.

Chemiresistive sensors, which are based on semiconducting materials, offer real-time monitoring of environment. However, detection of nonpolar chemical substances is often challenging because of the weakness of the doping effect. Herein, we report a concept of combining a cascade reaction (CR) and a chemiresistive sensor array for sensitive and selective detection of a target analyte (herein, ethylene in air). Ethylene was converted to acetaldehyde through a Pd-catalyzed heterogeneous Wacker reaction at 40 °C, followed by condensation with hydroxylamine hydrochloride to emit HCl vapor. HCl works as a strong dopant for single-walled carbon nanotubes (SWCNTs), enabling the main sensor to detect ethylene with excellent sensitivity (10.9% ppm-1) and limit of detection (0.2 ppm) in 5 min. False responses that occur in the main sensor are easily discriminated by reference sensors that partially employ CR. Moreover, though the sensor monitors the variation of normalized electric resistance (ΔR/R0) in the SWCNT network, temporary deactivation of CR yields a sensor system that does not require analyte-free air for a baseline correction (i.e., estimation of R0) and recovery of response. The concept presented here is generally applicable and offers a solution for several issues that are inherently present in chemiresistive sensing systems.

Estimation of the reproduction number and early prediction of the COVID-19 outbreak in India using a statistical computing approach.

Coronavirus disease 2019 (COVID-19), which causes severe respiratory illness, has become a pandemic. The World Health Organization has declared it a public health crisis of international concern. We developed a susceptible, exposed, infected, recovered (SEIR) model for COVID-19 to show the importance of estimating the reproduction number (R0). This work is focused on predicting the COVID-19 outbreak in its early stage in India based on an estimation of R0. The developed model will help policymakers to take active measures prior to the further spread of COVID-19. Data on daily newly infected cases in India from March 2, 2020 to April 2, 2020 were to estimate R0 using the earlyR package. The maximum-likelihood approach was used to analyze the distribution of R0 values, and the bootstrap strategy was applied for resampling to identify the most likely R0 value. We estimated the median value of R0 to be 1.471 (95% confidence interval [CI], 1.351 to 1.592) and predicted that the new case count may reach 39,382 (95% CI, 34,300 to 47,351) in 30 days.

Forecasting the 2017/2018 seasonal influenza epidemic in England using multiple dynamic transmission models: a case study

BackgroundSince the 2009 A/H1N1 pandemic, Public Health England have developed a suite of real-time statistical models utilising enhanced pandemic surveillance data to nowcast and forecast a future pandemic. Their ability to track seasonal influenza and predict heightened winter healthcare burden in the light of high activity in Australia in 2017 was untested.MethodsFour transmission models were used in forecasting the 2017/2018 seasonal influenza epidemic in England: a stratified primary care model using daily, region-specific, counts and virological swab positivity of influenza-like illness consultations in general practice (GP); a strain-specific (SS) model using weekly, national GP ILI and virological data; an intensive care model (ICU) using reports of ICU influenza admissions; and a synthesis model that included all data sources. For the first 12 weeks of 2018, each model was applied to the latest data to provide estimates of epidemic parameters and short-term influenza forecasts. The added value of pre-season population susceptibility data was explored.ResultsThe combined results provided valuable nowcasts of the state of the epidemic. Short-term predictions of burden on primary and secondary health services were initially highly variable before reaching consensus beyond the observed peaks in activity between weeks 3–4 of 2018. Estimates for R0 were consistent over time for three of the four models until week 12 of 2018, and there was consistency in the estimation of R0 across the SPC and SS models, and in the ICU attack rates estimated by the ICU and the synthesis model. Estimation and predictions varied according to the assumed levels of pre-season immunity.ConclusionsThis exercise successfully applied a range of pandemic models to seasonal influenza. Forecasting early in the season remains challenging but represents a crucially important activity to inform planning. Improved knowledge of pre-existing levels of immunity would be valuable.

The effect of control strategies to reduce social mixing on outcomes of the COVID-19 epidemic in Wuhan, China: a modelling study

SummaryBackgroundIn December, 2019, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a novel coronavirus, emerged in Wuhan, China. Since then, the city of Wuhan has taken unprecedented measures in response to the outbreak, including extended school and workplace closures. We aimed to estimate the effects of physical distancing measures on the progression of the COVID-19 epidemic, hoping to provide some insights for the rest of the world.MethodsTo examine how changes in population mixing have affected outbreak progression in Wuhan, we used synthetic location-specific contact patterns in Wuhan and adapted these in the presence of school closures, extended workplace closures, and a reduction in mixing in the general community. Using these matrices and the latest estimates of the epidemiological parameters of the Wuhan outbreak, we simulated the ongoing trajectory of an outbreak in Wuhan using an age-structured susceptible-exposed-infected-removed (SEIR) model for several physical distancing measures. We fitted the latest estimates of epidemic parameters from a transmission model to data on local and internationally exported cases from Wuhan in an age-structured epidemic framework and investigated the age distribution of cases. We also simulated lifting of the control measures by allowing people to return to work in a phased-in way and looked at the effects of returning to work at different stages of the underlying outbreak (at the beginning of March or April).FindingsOur projections show that physical distancing measures were most effective if the staggered return to work was at the beginning of April; this reduced the median number of infections by more than 92% (IQR 66–97) and 24% (13–90) in mid-2020 and end-2020, respectively. There are benefits to sustaining these measures until April in terms of delaying and reducing the height of the peak, median epidemic size at end-2020, and affording health-care systems more time to expand and respond. However, the modelled effects of physical distancing measures vary by the duration of infectiousness and the role school children have in the epidemic.InterpretationRestrictions on activities in Wuhan, if maintained until April, would probably help to delay the epidemic peak. Our projections suggest that premature and sudden lifting of interventions could lead to an earlier secondary peak, which could be flattened by relaxing the interventions gradually. However, there are limitations to our analysis, including large uncertainties around estimates of R0 and the duration of infectiousness.FundingBill & Melinda Gates Foundation, National Institute for Health Research, Wellcome Trust, and Health Data Research UK.

Early in the epidemic: impact of preprints on global discourse about COVID-19 transmissibility

Early in the epidemic: impact of preprints on global discourse about COVID-19 transmissibility

The Effect of Control Strategies that Reduce Social Mixing on Outcomes of the COVID-19 Epidemic in Wuhan, China

Background: In December 2019, a novel strain of SARS-CoV-2 emerged in Wuhan, China. Since then, the city of Wuhan has taken unprecedented measures and efforts in response to the outbreak. Methods: We quantified the effects of control measures on population contact patterns in Wuhan, China, to assess their effects on the progression of the outbreak. We included the latest estimates of epidemic parameters from a transmission model fitted to data on local and internationally exported cases from Wuhan in the age-structured epidemic framework. Further, we looked at the age-distribution of cases. Lastly, we simulated lifting of the control measures by allowing people to return to work in a phased-in way, and looked at the effects of returning to work at different stages of the underlying outbreak. Findings: Changes in mixing patterns may have contributed to reducing the number of infections in mid-2020 by 92% (interquartile range: 66–97%). There are benefits to sustaining these measures until April in terms of reducing the height of the peak, overall epidemic size in mid-2020 and probability that a second peak may occur after return to work. However, the modelled effects of social distancing measures vary by the duration of infectiousness and the role school children play in the epidemic. Interpretation: Restrictions on activities in Wuhan, if maintained until April, would likely contribute to the reduction and delay the epidemic size and peak, respectively. However, there are some limitations to the analysis, including large uncertainties around estimates of R0 and the duration of infectiousness.Funding: KP, YL, MJ, and PK were funded by the Bill & Melinda Gates Foundation (grant number INV003174), YL and MJ were funded by the National Institute for Health Research (NIHR) (16/137/109), TWR and AJK were funded by the Wellcome Trust (grant number 206250/Z/17/Z), RME was funded by HDR UK (grant number MR/S003975/1), and ND was funded by NIHR (HPRU-2012-10096).This research was partly funded by the National Institute for Health Research (NIHR) (16/137/109) using UK aid from the UK Government to support global health research. The views expressed in this publication are those of the author(s) and not necessarily those of the NIHR or the UK Department of Health and Social Care. We would like to acknowledge (in a randomised order) the other members of the London School of Hygiene & Tropical Medicine COVID-19 modelling group, who contributed to this work: Stefan Flasche, Samuel Clifford, Carl A B Pearson, James D Munday, Sam Abbott, Hamish Gibbs, Alicia Rosello, Billy J Quilty, Thibaut Jombart, Fiona Sun, Charlie Diamond, Amy Gimma, Kevin van Zandvoort, Sebastian Funk, Christopher I Jarvis, W John Edmunds, Nikos I Bosse, and Joel Hellewell. Their funding sources are as follows: Stefan Flasche and Sam Clifford (Sir Henry Dale Fellowship [grant number 208812/Z/17/Z]); Billy J Quilty, Fiona Sun, and Charlie Diamond (NIHR [grant number 16/137/109]); Joel Hellewell, Sam Abbott, James D Munday, and Sebastian Funk (Wellcome Trust [grant number 210758/Z/18/Z] ); Amy Gimma and Christopher I Jarvis (Global Challenges Research Fund [grant number ES/P010873/1]); Hamish Gibbs (Department of Health and Social Care [grant number ITCRZ 03010]); Alicia Rosello (NIHR [grant number PROD-1017-20002]); Thibaut Jombart (RCUK/ESRC [grant number ES/P010873/1], UK PH RST, NIHR HPRU Modelling Methodology); Kevin van Zandvoort (Elrha’s Research for Health in Humanitarian Crises (R2HC) Programme, UK Government (DFID), Wellcome Trust, NIHR).Declaration of Interest: The authors declare no competing interests.

The transmissibility of novel Coronavirus in the early stages of the 2019-20 outbreak in Wuhan: Exploring initial point-source exposure sizes and durations using scenario analysis

Background: The current novel coronavirus outbreak appears to have originated from a point-source exposure event at Huanan seafood wholesale market in Wuhan, China. There is still uncertainty around the scale and duration of this exposure event. This has implications for the estimated transmissibility of the coronavirus and as such, these potential scenarios should be explored. Methods: We used a stochastic branching process model, parameterised with available data where possible and otherwise informed by the 2002-2003 Severe Acute Respiratory Syndrome (SARS) outbreak, to simulate the Wuhan outbreak. We evaluated scenarios for the following parameters: the size, and duration of the initial transmission event, the serial interval, and the reproduction number (R0). We restricted model simulations based on the number of observed cases on the 25th of January, accepting samples that were within a 5% interval on either side of this estimate. Results: Using a pre-intervention SARS-like serial interval suggested a larger initial transmission event and a higher R0 estimate. Using a SARs-like serial interval we found that the most likely scenario produced an R0 estimate between 2-2.7 (90% credible interval (CrI)). A pre-intervention SARS-like serial interval resulted in an R0 estimate between 2-3 (90% CrI). There were other plausible scenarios with smaller events sizes and longer duration that had comparable R0 estimates. There were very few simulations that were able to reproduce the observed data when R0 was less than 1. Conclusions: Our results indicate that an R0 of less than 1 was highly unlikely unless the size of the initial exposure event was much greater than currently reported. We found that R0 estimates were comparable across scenarios with decreasing event size and increasing duration. Scenarios with a pre-intervention SARS-like serial interval resulted in a higher R0 and were equally plausible to scenarios with SARs-like serial intervals.

The transmissibility of novel Coronavirus in the early stages of the 2019-20 outbreak in Wuhan: Exploring initial point-source exposure sizes and durations using scenario analysis.

Background: The current novel coronavirus outbreak appears to have originated from a point-source exposure event at Huanan seafood wholesale market in Wuhan, China. There is still uncertainty around the scale and duration of this exposure event. This has implications for the estimated transmissibility of the coronavirus and as such, these potential scenarios should be explored. Methods: We used a stochastic branching process model, parameterised with available data where possible and otherwise informed by the 2002-2003 Severe Acute Respiratory Syndrome (SARS) outbreak, to simulate the Wuhan outbreak. We evaluated scenarios for the following parameters: the size, and duration of the initial transmission event, the serial interval, and the reproduction number (R0). We restricted model simulations based on the number of observed cases on the 25th of January, accepting samples that were within a 5% interval on either side of this estimate. Results: Using a pre-intervention SARS-like serial interval suggested a larger initial transmission event and a higher R0 estimate. Using a SARs-like serial interval we found that the most likely scenario produced an R0 estimate between 2-2.7 (90% credible interval (CrI)). A pre-intervention SARS-like serial interval resulted in an R0 estimate between 2-3 (90% CrI). There were other plausible scenarios with smaller events sizes and longer duration that had comparable R0 estimates. There were very few simulations that were able to reproduce the observed data when R0 was less than 1. Conclusions: Our results indicate that an R0 of less than 1 was highly unlikely unless the size of the initial exposure event was much greater than currently reported. We found that R0 estimates were comparable across scenarios with decreasing event size and increasing duration. Scenarios with a pre-intervention SARS-like serial interval resulted in a higher R0 and were equally plausible to scenarios with SARs-like serial intervals.

Preliminary estimation of the basic reproduction number of novel coronavirus (2019-nCoV) in China, from 2019 to 2020: A data-driven analysis in the early phase of the outbreak

BackgroundsAn ongoing outbreak of a novel coronavirus (2019-nCoV) pneumonia hit a major city in China, Wuhan, December 2019 and subsequently reached other provinces/regions of China and other countries. We present estimates of the basic reproduction number, R0, of 2019-nCoV in the early phase of the outbreak. MethodsAccounting for the impact of the variations in disease reporting rate, we modelled the epidemic curve of 2019-nCoV cases time series, in mainland China from January 10 to January 24, 2020, through the exponential growth. With the estimated intrinsic growth rate (γ), we estimated R0 by using the serial intervals (SI) of two other well-known coronavirus diseases, MERS and SARS, as approximations for the true unknown SI. FindingsThe early outbreak data largely follows the exponential growth. We estimated that the mean R0 ranges from 2.24 (95%CI: 1.96–2.55) to 3.58 (95%CI: 2.89–4.39) associated with 8-fold to 2-fold increase in the reporting rate. We demonstrated that changes in reporting rate substantially affect estimates of R0. ConclusionThe mean estimate of R0 for the 2019-nCoV ranges from 2.24 to 3.58, and is significantly larger than 1. Our findings indicate the potential of 2019-nCoV to cause outbreaks.

Taking Account of Asymptomatic Infections in Modeling the Transmission Potential of the COVID-19 Outbreak on the Diamond Princess Cruise Ship

Background: The COVID-19 outbreak on the Diamond Princess (DP) cruise ship provided empirical data to study the transmission potential of COVID-19 under quarantine with the presence of asymptomatic cases. Methods: We studied the changes in R0 on the DP from January 21 to February 19, 2020 based on chain-binomial models under two scenarios: no quarantine assuming a random mixing condition, and quarantine of passengers in cabins — passengers may get infected either by an infectious case in a shared cabin or by asymptomatic crew who continued to work. Results: Estimates of R0 at the beginning of the epidemic were 3.27 (95% CI, 3.02-3.54) and 3.78 (95% CI, 3.49-4.09) respectively for serial intervals of 5 and 6 days; and when quarantine started, with the reported asymptomatic ratio 0.505, R0 rose to 4.18 (95%CI, 3.86-4.52) and 4.73 (95%CI, 4.37-5.12) respectively for passengers who might be exposed to the virus due to contacts with asymptomatic crew. The overall R0 for both crew and passengers was decreased to 2.55 (95%CI, 2.36-2.76) and 2.90 (95% CI, 2.67-3.13). Results show that the higher the asymptomatic ratio is, the more infectious contacts would happen. Conclusions: We find evidence to support a US CDC report that “a high proportion of asymptomatic infections could partially explain the high attack rate among cruise ship passengers and crew.” Our study suggests that the effects of quarantine may be limited if the asymptomatic ratio is high, implying that a combination of preventive measures is needed to stop the spread of virus. .

On (the Lack of) Identification of the SEIRD Epidemic Model for COVID-19, Its Estimation, and Related Problems

This paper studies the SEIRD epidemic model for COVID-19. First, I show that the model is poorly identified from the observed number of deaths and confirmed cases. There are many sets of parameters that are observationally equivalent in the short run but lead to markedly different long run forecasts. Second, I show that the basic reproduction number R0 can be identified from the data, conditional on epidemiologic parameters, and propose several nonlinear SUR approaches to estimate R0. I study the performance of these methods using Monte Carlo studies and demonstrate that they yield fairly accurate estimates of R0. Next, I apply these methods to estimate R0 for the US, California, and Japan, and document heterogeneity in the value of R0 across regions. My estimation approach accounts for possible underreporting of the number of cases. I demonstrate that if one fails to take underreporting into account and estimates R0 from the reported cases data, the resulting estimate of R0 may be biased downward and the resulting forecasts may exaggerate the long run number of deaths. Finally, I discuss how auxiliary information from random tests can be used to calibrate the initial parameters of the model and narrow down the range of possible forecasts of the future number of deaths.

2021 Olympic Games Tokyo: Safety Issues and Protection against COVID-19 Transmission.

Sir, Tokyo will soon host one of the world's biggest sporting events, Olympic Games that are expected to happen on July 23, 2021. This event is a great opportunity and potential for triggering a new global dissemination of not only the coronavirus but also bacteria containing successful antimicrobial drug-resistant determinants. One recent study published by Kissler et al.[1] highlighted the problem. According to this publication, even in the event of apparent elimination after the initial, most severe pandemic wave, SARS-CoV-2 surveillance should be maintained since resurgence in contagion could be possible as late as 2024. One of the risks in large concentrations is the presence of someone capable of infecting a considerable number of people, and it can be a calamity considering that the initial estimates of R0 for SARS-CoV-2 vary from 1.6 to 4.1.[23] In addition, the strong infectivity of SARS-CoV-2 and the rapid asymptomatic transmission during the long incubation period have been precisely demonstrated.[4] The treatment of COVID-19 is mainly focused on the rigorous implementation of public health measures, since SARS-CoV-2 infections do not yet have antiviral therapy and effective vaccines. In addition, another concern that is being considered in recent years has been regarding ease of transmission of respiratory-borne diseases in an air cabin environment.[5] In this context, the risk of international spread of COVID-19 for several countries is imminent and worrisome because we may be facing a second infection curve in the near future. These risks and their implications for public health are often overlooked. They are very important and should be considered by the WHO and other health authorities in advance of a sporting global event. Since an understanding of the risk of infection with SARS-CoV-2 is well known in the world, prevention and accompaniment of travelers measures can be discussed when an event of this size will be held. The Olympic Games Tokyo 2021 will undoubtedly ensure that the stadiums will be crowded with spectators. The risk of exposu to COVID-19 among other communicable diseases through close contact both for travelers and spectators attending the event themselves would support the recommendation of the post-Olympic Games surveillance system. This measure would be essential to prevent the international travel of possible epidemic microorganisms, as well as ensure the isolation of those with symptoms for COVID-19. Although scientists do not believe that a related COVID-19 epidemic will spread rapidly and intensively in Japan, the COVID-19 pandemic will be able to test again world health systems; however, the world is more secure when safe measures are taken from the experience gained. Financial support and sponsorship This study was supported by CNPq and FAPEMIG. This letter was based on observations and reflections about COVID-19 pandemic during studies supported by the funding agencies. Conflicts of interest There are no conflicts of interest.

R0 Estimation for the African Swine Fever Epidemics in Wild Boar of Czech Republic and Belgium.

African swine fever (ASF) is a contagious haemorrhagic fever that affects both domesticated and wild pigs. Since ASF reached Europe wild boar populations have been a reservoir for the virus. Collecting reliable data on infected individuals in wild populations is challenging, and this makes it difficult to deploy an effective eradication strategy. However, for diseases with high lethality rate, infected carcasses can be used as a proxy for the number of infected individuals at a certain time. Then R0 parameter can be used to estimate the time distribution of the number of newly infected individuals for the outbreak. We estimated R0 for two ASF outbreaks in wild boar, in Czech Republic and Belgium, using the exponential growth method. This allowed us to estimate both R0 and the doubling time (Td) for those infections. The results are R0 = 1.95, Td = 4.39 for Czech Republic and R0 = 1.65, Td = 6.43 for Belgium. We suggest that, if estimated as early as possible, R0 and Td can provide an expected course for the infection against which to compare the actual data collected in the field. This would help to assess if passive surveillance is properly implemented and hence to verify the efficacy of the applied control measures.

Сomparison of on-line and off-line Fried parameter estimation methods

We address the problem of high-accuracy estimation of the Fried parameter r0 by comparing two approaches based on estimation of statistical properties of intensity-based wavefront measurements with parameter-fitting to the theoretically predicted values. In the first approach, the phase of the aberration-degraded field is restored from the measurements to obtain the statistical estimate for the structure function. Due to the iterative nature of the most phase retrieval methods, this approach requires significant computational time and thus cannot provide results in real time. In the second approach, the structure function of the sub-aperture wavefront slopes is directly calculated and related to the turbulence parameters in real time. We describe the equations used to obtain the estimate of the Fried parameter by both methods and check their accuracy with numerical simulations.

Correlation of the basic reproduction number (R0) and eco-environmental variables in Colombian municipalities with chikungunya outbreaks during 2014-2016.

Chikungunya virus (CHIKV) emerged in Colombia in 2014 into a population presumed fully susceptible. This resulted in a quick and intense spread across Colombia, resulting in an epidemic that affected an estimated 450,000 people. The reported Colombian cases accounted for over 49% of all the cases reported to the PAHO. Eco-environmental factors are known to be associated with the spread of arboviruses such as CHIKV, and likely contribute to the differences in transmission profiles that were observed across several municipalities. To determine the association of eco-environmental factors and CHIKV, the basic reproduction number (R0) in 85 municipalities, which accounted for 65.6% of reported CHIKV cases in Colombia, was estimated. Estimates of R0 ranged from 1 to 9, where over 76% of municipalities had R0 values between 1 and 2. When we looked at the distribution of R0, the cumulative proportions were 20% with R0>2, 14% with R0>3, and 9% with R0>4. Next, we determined that there were different patterns of correlation between environmental and/or ecological variables and R0 when we considered different R0 lower-thresholds. Broadly, we found that temperature-related variables are significantly and positively correlated to R0 regardless of the lower threshold, while other variables like duration of outbreak and size of the urban area are inversely related to R0. Specifically, we conclude that high values of temperature-related variables where R0 > 1 will result in a fast growth of cases in a shorter time period (with faster cessation of outbreak transmission) but will result overall in a fewer total cases compared to outbreak areas (R0 > 1, but classified as lower). Thus, in the absence of vector control, a less explosive outbreak may be more advantageous for the virus in terms of transmission.

Physics-based model of the adaptive-optics-corrected point spread function

Context.Adaptive optics (AO) systems greatly increase the resolution of large telescopes, but produce complex point spread function (PSF) shapes, varying in time and across the field of view. The PSF must be accurately known since it provides crucial information about optical systems for design, characterization, diagnostics, and image post-processing.Aims.We develop here a model of the AO long-exposure PSF, adapted to various seeing conditions and any AO system. This model is made to match accurately both the core of the PSF and its turbulent halo.Methods.The PSF model we develop is based on a parsimonious parameterization of the phase power spectral density, with only five parameters to describe circularly symmetric PSFs and seven parameters for asymmetrical ones. Moreover, one of the parameters is the Fried parameterr0of the turbulence’s strength. This physical parameter is an asset in the PSF model since it can be correlated with external measurements of ther0, such as phase slopes from the AO real time computer (RTC) or site seeing monitoring.Results.We fit our model against end-to-end simulated PSFs using the OOMAO tool, and against on-sky PSFs from the SPHERE/ZIMPOL imager and the MUSE integral field spectrometer working in AO narrow-field mode. Our model matches the shape of the AO PSF both in the core and the halo, with a relative error smaller than 1% for simulated and experimental data. We also show that we retrieve ther0parameter with sub-centimeter precision on simulated data. For ZIMPOL data, we show a correlation of 97% between ourr0estimation and the RTC estimation. Finally, MUSE allows us to test the spectral dependency of the fittedr0parameter. It follows the theoreticalλ6/5evolution with a standard deviation of 0.3 cm. Evolution of other PSF parameters, such as residual phase variance or aliasing, is also discussed.

Effects of climate change on reproductive number of Chagas disease

Reproductive number (R0)-maps estimate risk zones of vector-borne diseases and geographical distribution changes under climate change. To map R0 aiming to estimate the epidemiological risk of Chagas disease in Chile, its distribution and possible changes due to the global climate change. We used a relationship between R0 and entomological parameters of vectors as function of environmental variables, to map the risk of Chagas disease in Chile, under current and projected future environmental conditions. We obtained a geographical R0 estimation of Chagas disease in Chile. The highest R0averages correspond to the Central-Northern regions of Chile. T. cruzi transmission area could increase in the future due to climate changes. Independent of the future condition, both for optimistic and pessimistic climate change scenarios, the area of potential risk for Chagas disease transmission would increase. The estimated R0 values suggest that, if a control of T. infestans is not maintained, Chagas disease endemic status will persist or increase, independently of the climate change scenarios. Mapping R0 values is an effective method to assess the risk of Chagas disease. The eventual increase in the transmission area of the disease is worrisome.