Using a stochastic continuous-time Markov chain model to examine alternative timing and duration of the COVID-19 lockdown in Kuwait: what can be done now?

Background Kuwait had its first COVID-19 in late February, and until October 6, 2020 it recorded 108,268 cases and 632 deaths. Despite implementing one of the strictest control measures-including a three-week complete lockdown, there was no sign of a declining epidemic curve. The objective of the current analyses is to determine, hypothetically, the optimal timing and duration of a full lockdown in Kuwait that would result in controlling new infections and lead to a substantial reduction in case hospitalizations. Methods The analysis was conducted using a stochastic Continuous-Time Markov Chain (CTMC), eight state model that depicts the disease transmission and spread of SARS-CoV 2. Transmission of infection occurs between individuals through social contacts at home, in schools, at work, and during other communal activities. Results The model shows that a lockdown 10 days before the epidemic peak for 90 days is optimal but a more realistic duration of 45 days can achieve about a 45% reduction in both new infections and case hospitalizations. Conclusions In the view of the forthcoming waves of the COVID19 pandemic anticipated in Kuwait using a correctly-timed and sufficiently long lockdown represents a workable management strategy that encompasses the most stringent form of social distancing with the ability to significantly reduce transmissions and hospitalizations.

Estimation the state of the Covid-19 epidemic curve in Mayotte

We study in this work some statistical methods to estimate the parameters resulting from the use of an age-structured contact mathematical epidemic model in order to analyze the evolution of the epidemic curve of Covid-19 in the French overseas department Mayotte from march 13, 2020 to february 26,2021. Using several statistic methods based on time dependent method, maximum likelihood, mixture method, we fit the probability distribution which underlines the serial interval distribution and we give an adapted version of the generation time distribution from Package R0. The best-fit model of the serial interval was given by a mixture of Weibull distribution. Furthermore this estimation allows to obtain the evolution of the time varying effective reproduction number and hence the temporal transmission rates. Finally based on others known estimates parameters we incorporate the estimated parameters in the model in order to give an approximation of the epidemic curve in Mayotte under the conditions of the model. We also discuss the limit of our study and the conclusion concerned a probable impact of non pharmacological interventions of the Covid-19 in Mayotte such us the re-infection cases and the introduction of the variants which probably affect the estimates.

Mathematical modelling of COVID-19 vaccination strategies in Kyrgyzstan

Objectives: In December 2020, an unprecedented vaccination programme to deal with the COVID-19 pandemic was initiated worldwide. However, the vaccine provision is currently insufficient for most countries to vaccinate their entire eligible population, so it is essential to develop the most efficient vaccination strategies. COVID-19 disease severity and mortality vary by age, therefore age-dependent vaccination strategies must be developed. Study design and Methods: Here, we use an age-dependent SIERS (susceptible, infected, exposed, recovered, susceptible), deterministic model, to compare four hypothetical age-dependent vaccination strategies and their potential impact on the COVID-19 epidemic in Kyrgyzstan. Results: Over the short-term (until March 2022), a vaccination rollout strategy focussed on high-risk groups (aged greater than 50 years) with some vaccination among high-incidence groups (aged 20 to 49 years) may decrease symptomatic cases and COVID-19-attributable deaths. However, there will be limited impact on the estimated overall number of COVID19 cases with the relatively low coverage of high incidence groups (15 to 25% based on current vaccine availability). Vaccination plus nonpharmaceutical interventions (NPIs), such as mask-wearing and social distancing will further decrease COVID-19 incidence and mortality and may have an indirect impact on all-cause mortality. Conclusions: Our results and other evidence suggest that vaccination is most effective in flattening the epidemic curve and reducing mortality if supported by NPIs. In the short term, focussing on high-risk groups may reduce the burden on the health system and result in fewer deaths. However, the herd effect from delaying another peak may only be achieved by greater vaccination coverage in high incidence groups.

Signatures of Cholera Outbreak during the Yemeni Civil War, 2016–2019

The Global Task Force on Cholera Control (GTFCC) created a strategy for early outbreak detection, hotspot identification, and resource mobilization coordination in response to the Yemeni cholera epidemic. This strategy requires a systematic approach for defining and classifying outbreak signatures, or the profile of an epidemic curve and its features. We used publicly available data to quantify outbreak features of the ongoing cholera epidemic in Yemen and clustered governorates using an adaptive time series methodology. We characterized outbreak signatures and identified clusters using a weekly time series of cholera rates in 20 Yemeni governorates and nationally from 4 September 2016 through 29 December 2019 as reported by the World Health Organization (WHO). We quantified critical points and periods using Kolmogorov–Zurbenko adaptive filter methodology. We assigned governorates into six clusters sharing similar outbreak signatures, according to similarities in critical points, critical periods, and the magnitude of peak rates. We identified four national outbreak waves beginning on 12 September 2016, 6 March 2017, 28 May 2018, and 28 January 2019. Among six identified clusters, we classified a core regional hotspot in Sana’a, Sana’a City, and Al-Hudaydah—the expected origin of the national outbreak. The five additional clusters differed in Wave 2 and Wave 3 peak frequency, timing, magnitude, and geographic location. As of 29 December 2019, no governorates had returned to pre-Wave 1 levels. The detected similarity in outbreak signatures suggests potentially shared environmental and human-made drivers of infection; the heterogeneity in outbreak signatures implies the potential traveling waves outwards from the core regional hotspot that could be governed by factors that deserve further investigation.

Embedding Isolation, Contact Tracing, and Quarantine in Transmission Dynamics of the Coronavirus Epidemic—A Case Study of COVID-19 in Wuhan

Coronaviruses have caused multiple global pandemics. As an emerging epidemic, the coronavirus disease relies on nonpharmacological interventions to control its spread. However, the specific effects of these interventions are unknown. To evaluate their effects, we extend the susceptible–latent–infectious–recovered model to include suspected cases, confirmed cases, and their contacts and to embed isolation, close contact tracing, and quarantine into transmission dynamics. The model simplifies the population into two parts: the undiscovered part (where the virus spreads freely—the extent of freedom is determined by the strength of social distancing policy) and the discovered part (where the cases are incompletely isolated or quarantined). Through the isolation of the index case (suspected or confirmed case) and the subsequent tracing and quarantine of its close contacts, the infections flow from the undiscovered part to the discovered part. In our case study, multisource data of the novel coronavirus SARS-CoV-2 (COVID-19) in Wuhan were collected to validate the model, the parameters were calibrated based on the prediction of the actual number of infections, and then the time-varying effective reproduction number was obtained to measure the transmissibility of COVID-19 in Wuhan, revealing the timeliness and lag effect of the nonpharmacological interventions adopted there. Finally, we simulated the situation in the absence of a strict social distancing policy. Results show that the current efforts of isolation, close contact tracing, and quarantine can take the epidemic curve to the turning point, but the epidemic could be far from over; there were still 4,035 infected people, and 1,584 latent people in the undiscovered part on March 11, 2020, when the epidemic was actually over with a strict social distancing policy.

Epidemic waves in relief against a structured climatic background

<p>For directly transmissible infectious diseases, seasonality in the course of epidemics may manifest in four major ways: susceptibility of the hosts, their individual and collective behavior, transmissibility of the pathogen, and survival of the latter under evolving environmental conditions. Mechanisms and concepts are not finally settled, notably in a pandemic setting. Climatic seasonality by itself is an aggregate, structured phenomenon that provides a spatially distributed background to the epidemic outbreak and its evolution at multiple timescales. Using advanced methods of data and systems analysis, including epidemiological and climate modeling, the RKI data of the COVID-19 epidemic curve for Berlin and a five-parameter climate data set of the nearby station Lindenberg (Mark) are analyzed in daily resolution over the period March 2020 to October 2021. Aimed to identify extrinsic impacts due to organized intraseasonal climate dynamics, as seen in sunshine duration and surface climate (pressure, temperature, humidity, wind), on intrinsic dynamics of the epidemic system, an established (SEIR) model of the latter and modifications thereof have been subjected to in-depth studies with a view on both genesis and timing of epidemic waves and their potential synchronization with climatic background dynamics. Starting with a case study for the spring 2020 period of shutdown, which unveils remarkable synchronies with the seasonal transition, estimates are given and applied to the follow-up period of individual and combined impacts of climate variables on the SEIR model in different oscillatory (non-equilibrium, lately endemic) regimes of operation.</p>

EpiLPS: a fast and flexible Bayesian tool for near real-time estimation of the time-varying reproduction number

In infectious disease epidemiology, the instantaneous reproduction number R(t) is a timevarying metric defined as the average number of secondary infections generated by individuals who are infectious at time t. It is therefore a crucial epidemiological parameter that assists public health decision makers in the management of an epidemic. We present a new Bayesian tool for robust estimation of the time-varying reproduction number. The proposed methodology smooths the epidemic curve and allows to obtain (approximate) point estimates and credible envelopes of R(t) by employing the renewal equation, using Bayesian P-splines coupled with Laplace approximations of the conditional posterior of the spline vector. Two alternative approaches for inference are presented: (1) an approach based on a maximum a posteriori argument for the model hyperparameters, delivering estimates of R(t) in only a few seconds; and (2) an approach based on a MCMC scheme with underlying Langevin dynamics for efficient sampling of the posterior target distribution. Case counts per unit of time are assumed to follow a Negative Binomial distribution to account for potential excess variability in the data that would not be captured by a classic Poisson model. Furthermore, after smoothing the epidemic curve, a “plug-in” estimate of the reproduction number can be obtained from the renewal equation yielding a closed form expression of R(t) as a function of the spline parameters. The approach is extremely fast and free of arbitrary smoothing assumptions. EpiLPS is applied on data of SARS-CoV-1 in Hong-Kong (2003), influenza A H1N1 (2009) in the USA and current SARS-CoV-2 pandemic (2020-2021) for Belgium, Portugal, Denmark and France.Author summaryThe instantaneous reproduction number R(t) is a key metric that provides important insights into an epidemic outbreak. We present a flexible Bayesian approach called EpiLPS (Epidemiological modeling with Laplacian-P-splines) for smooth estimation of the epidemic curve and R(t). Computational speed and absence of arbitrary assumptions on smoothing makes EpiLPS an interesting tool for near real-time estimation of the reproduction number. An R software package is available (https://github.com/oswaldogressani).

Cholera outbreak caused by drinking unprotected well water contaminated with faeces from an open storm water drainage: Kampala City, Uganda, January 2019

Background Kampala city slums, with one million dwellers living in poor sanitary conditions, frequently experience cholera outbreaks. On 6 January 2019, Rubaga Division notified the Uganda Ministry of Health of a suspected cholera outbreak in Sembule village. We investigated to identify the source and mode of transmission, and recommended evidence-based interventions. Methods We defined a suspected case as onset of profuse, painless, acute watery diarrhoea in a Kampala City resident (≥ 2 years) from 28 December 2018 to 11 February 2019. A confirmed case was a suspected case with Vibrio cholerae identified from the patient’s stool specimen by culture. We found cases by record review and active community case-finding. We conducted a case–control study in Sembule village, the epi-center of this outbreak, to compare exposures between confirmed case-persons and asymptomatic controls, individually matched by age group. We overlaid rainfall data with the epidemic curve to identify temporal patterns between rain and illnesses. We conducted an environmental assessment, interviewed village local council members, and tested water samples from randomly-selected households and water sources using culture and PCR to identify V. cholerae. Results We identified 50 suspected case-patients, with three deaths (case-fatality rate: 6.0%). Of 45 case-patients with stool samples tested, 22 were confirmed positive for V. cholerae O1, serotype Ogawa. All age groups were affected; persons aged 5–14 years had the highest attack rate (AR) (8.2/100,000). The epidemic curve showed several point-source outbreaks; cases repeatedly spiked immediately following rainfall. Sembule village had a token-operated water tap, which had broken down 1 month before the outbreak, forcing residents to obtain water from one of three wells (Wells A, B, C) or a public tap. Environmental assessment showed that residents emptied their feces into a drainage channel connected to Well C. Drinking water from Well C was associated with illness (ORM–H = 21, 95% CI 4.6–93). Drinking water from a public tap (ORM–H = 0.07, 95% CI 0.014–0.304) was protective. Water from a container in one of eight households sampled tested positive for V. cholerae; water from Well C had coliform counts ˃ 900/100 ml. Conclusions Drinking contaminated water from an unprotected well was associated with this cholera outbreak. We recommended emergency chlorination of drinking water, fixing the broken token tap, and closure of Well C.

Vaccination as an Alternative to Non-Drug Interventions to Prevent Local Resurgence of COVID-19

Background While a COVID-19 vaccine protects people from serious illness and death, it remains concern when and how to relax from the high cost strict non-pharmaceutical interventions (NPIs). Methods We developed a stochastic calculus model to identify the level of vaccine coverage that would allow safe relaxation of NPIs, and the vaccination strategies that can best achieve this level of coverage. We applied Monto Carlo simulations more than 10,000 times to remove random fluctuation effects and obtain fitted/predicted epidemic curve based on various parameters with 95% confidence interval (95% CI) at each time point. Results We found that a vaccination coverage of 50.42% was needed for the safe relaxation of NPIs, if the vaccine effectiveness was 79.34%. However, with the increasing of variants transmissibility and the decline of vaccine effectiveness for variants, the threshold for lifting NPIs would be higher. We estimated that more than 8 months were needed to achieve the vaccine coverage threshold in the combination of accelerated vaccination strategy and key groups firstly strategy. Conclusion If there are sufficient doses of vaccine then an accelerated vaccination strategy should be used, and if vaccine supply is insufficient then high-risk groups should be targeted for vaccination first. Sensitivity analyses results shown that the higher the transmission rate of the virus and the lower annual vaccine supply, the more difficult the epidemic could be under control. In conclusion, as vaccine coverage improves, the NPIs can be gradually relaxed. Until that threshold is reached, however, strict NPIs are still needed to contain the epidemic. The more transmissible SARS-CoV-2 variant lead to higher resurgence probability, which indicates the importance of accelerated vaccination and achieving the vaccine coverage earlier. Trial registration We did not involve clinical trial.

The effectiveness of Non-pharmaceutical interventions in reducing the COVID-19 contagion in the UK, an observational and modelling study

Epidemiological models used to inform government policies aimed to reduce the contagion of COVID-19, assume that the reproduction number is reduced through Non-Pharmaceutical Interventions (NPIs) leading to physical distancing. Available data in the UK show an increase in physical distancing before the NPIs were implemented and a fall soon after implementation. We aimed to estimate the effect of people’s behaviour on the epidemic curve and the effect of NPIs taking into account this behavioural component. We have estimated the effects of confirmed daily cases on physical distancing and we used this insight to design a behavioural SEIR model (BeSEIR), simulated different scenaria regarding NPIs and compared the results to the standard SEIR. Taking into account behavioural insights improves the description of the contagion dynamics of the epidemic significantly. The BeSEIR predictions regarding the number of infections without NPIs were several orders of magnitude less than the SEIR. However, the BeSEIR prediction showed that early measures would still have an important influence in the reduction of infections. The BeSEIR model shows that even with no intervention the percentage of the cumulative infections within a year will not be enough for the epidemic to resolve due to a herd immunity effect. On the other hand, a standard SEIR model significantly overestimates the effectiveness of measures. Without taking into account the behavioural component, the epidemic is predicted to be resolved much sooner than when taking it into account and the effectiveness of measures are significantly overestimated.