SpatialWavePredict: a tutorial-based primer and toolbox for forecasting growth trajectories using the ensemble spatial wave sub-epidemic modeling framework.

BACKGROUND: Dynamical mathematical models defined by a system of differential equations are typically not easily accessible to non-experts. However, forecasts based on these types of models can help gain insights into the mechanisms driving the process and may outcompete simpler phenomenological growth models. Here we introduce a friendly toolbox, SpatialWavePredict, to characterize and forecast the spatial wave sub-epidemic model, which captures diverse wave dynamics by aggregating multiple asynchronous growth processes and has outperformed simpler phenomenological growth models in short-term forecasts of various infectious diseases outbreaks including SARS, Ebola, and the early waves of the COVID-19 pandemic in the US. RESULTS: This tutorial-based primer introduces and illustrates a user-friendly MATLAB toolbox for fitting and forecasting time-series trajectories using an ensemble spatial wave sub-epidemic model based on ordinary differential equations. Scientists, policymakers, and students can use the toolbox to conduct real-time short-term forecasts. The five-parameter epidemic wave model in the toolbox aggregates linked overlapping sub-epidemics and captures a rich spectrum of epidemic wave dynamics, including oscillatory wave behavior and plateaus. An ensemble strategy aims to improve forecasting performance by combining the resulting top-ranked models. The toolbox provides a tutorial for forecasting time-series trajectories, including the full uncertainty distribution derived through parametric bootstrapping, which is needed to construct prediction intervals and evaluate their accuracy. Functions are available to assess forecasting performance, estimation methods, error structures in the data, and forecasting horizons. The toolbox also includes functions to quantify forecasting performance using metrics that evaluate point and distributional forecasts, including the weighted interval score. CONCLUSIONS: We have developed the first comprehensive toolbox to characterize and forecast time-series data using an ensemble spatial wave sub-epidemic wave model. As an epidemic situation or contagion occurs, the tools presented in this tutorial can facilitate policymakers to guide the implementation of containment strategies and assess the impact of control interventions. We demonstrate the functionality of the toolbox with examples, including a tutorial video, and is illustrated using daily data on the COVID-19 pandemic in the USA.

Markovian Approach for Exploring Competitive Diseases with Heterogeneity-Evidence from COVID-19 and Influenza in China.

Due to the complex interactions between multiple infectious diseases, the spreading of diseases in human bodies can vary when people are exposed to multiple sources of infection at the same time. Typically, there is heterogeneity in individuals' responses to diseases, and the transmission routes of different diseases also vary. Therefore, this paper proposes an SIS disease spreading model with individual heterogeneity and transmission route heterogeneity under the simultaneous action of two competitive infectious diseases. We derive the theoretical epidemic spreading threshold using quenched mean-field theory and perform numerical analysis under the Markovian method. Numerical results confirm the reliability of the theoretical threshold and show the inhibitory effect of the proportion of fully competitive individuals on epidemic spreading. The results also show that the diversity of disease transmission routes promotes disease spreading, and this effect gradually weakens when the epidemic spreading rate is high enough. Finally, we find a negative correlation between the theoretical spreading threshold and the average degree of the network. We demonstrate the practical application of the model by comparing simulation outputs to temporal trends of two competitive infectious diseases, COVID-19 and seasonal influenza in China.

Threshold dynamics of a reaction-advection-diffusion schistosomiasis epidemic model with seasonality and spatial heterogeneity.

Most water-borne disease models ignore the advection of water flows in order to simplify the mathematical analysis and numerical computation. However, advection can play an important role in determining the disease transmission dynamics. In this paper, we investigate the long-term dynamics of a periodic reaction-advection-diffusion schistosomiasis model and explore the joint impact of advection, seasonality and spatial heterogeneity on the transmission of the disease. We derive the basic reproduction number R 0 and show that the disease-free periodic solution is globally attractive when R 0 < 1 whereas there is a positive endemic periodic solution and the system is uniformly persistent in a special case when R 0 > 1 . Moreover, we find that R 0 is a decreasing function of the advection coefficients which offers insights into why schistosomiasis is more serious in regions with slow water flows.

Modelling the impact of precaution on disease dynamics and its evolution.

In this paper, we introduce the notion of practically susceptible population, which is a fraction of the biologically susceptible population. Assuming that the fraction depends on the severity of the epidemic and the public's level of precaution (as a response of the public to the epidemic), we propose a general framework model with the response level evolving with the epidemic. We firstly verify the well-posedness and confirm the disease's eventual vanishing for the framework model under the assumption that the basic reproduction number R 0 < 1 . For R 0 > 1 , we study how the behavioural response evolves with epidemics and how such an evolution impacts the disease dynamics. More specifically, when the precaution level is taken to be the instantaneous best response function in literature, we show that the endemic dynamic is convergence to the endemic equilibrium; while when the precaution level is the delayed best response, the endemic dynamic can be either convergence to the endemic equilibrium, or convergence to a positive periodic solution. Our derivation offers a justification/explanation for the best response used in some literature. By replacing "adopting the best response" with "adapting toward the best response", we also explore the adaptive long-term dynamics.

DeepDynaForecast: Phylogenetic-informed graph deep learning for epidemic transmission dynamic prediction.

In the midst of an outbreak or sustained epidemic, reliable prediction of transmission risks and patterns of spread is critical to inform public health programs. Projections of transmission growth or decline among specific risk groups can aid in optimizing interventions, particularly when resources are limited. Phylogenetic trees have been widely used in the detection of transmission chains and high-risk populations. Moreover, tree topology and the incorporation of population parameters (phylodynamics) can be useful in reconstructing the evolutionary dynamics of an epidemic across space and time among individuals. We now demonstrate the utility of phylodynamic trees for transmission modeling and forecasting, developing a phylogeny-based deep learning system, referred to as DeepDynaForecast. Our approach leverages a primal-dual graph learning structure with shortcut multi-layer aggregation, which is suited for the early identification and prediction of transmission dynamics in emerging high-risk groups. We demonstrate the accuracy of DeepDynaForecast using simulated outbreak data and the utility of the learned model using empirical, large-scale data from the human immunodeficiency virus epidemic in Florida between 2012 and 2020. Our framework is available as open-source software (MIT license) at github.com/lab-smile/DeepDynaForcast.

An immuno-epidemiological model with waning immunity after infection or vaccination.

In epidemics, waning immunity is common after infection or vaccination of individuals. Immunity levels are highly heterogeneous and dynamic. This work presents an immuno-epidemiological model that captures the fundamental dynamic features of immunity acquisition and wane after infection or vaccination and analyzes mathematically its dynamical properties. The model consists of a system of first order partial differential equations, involving nonlinear integral terms and different transfer velocities. Structurally, the equation may be interpreted as a Fokker-Planck equation for a piecewise deterministic process. However, unlike the usual models, our equation involves nonlocal effects, representing the infectivity of the whole environment. This, together with the presence of different transfer velocities, makes the proved existence of a solution novel and nontrivial. In addition, the asymptotic behavior of the model is analyzed based on the obtained qualitative properties of the solution. An optimal control problem with objective function including the total number of deaths and costs of vaccination is explored. Numerical results describe the dynamic relationship between contact rates and optimal solutions. The approach can contribute to the understanding of the dynamics of immune responses at population level and may guide public health policies.

Informing policy via dynamic models: Cholera in Haiti.

Public health decisions must be made about when and how to implement interventions to control an infectious disease epidemic. These decisions should be informed by data on the epidemic as well as current understanding about the transmission dynamics. Such decisions can be posed as statistical questions about scientifically motivated dynamic models. Thus, we encounter the methodological task of building credible, data-informed decisions based on stochastic, partially observed, nonlinear dynamic models. This necessitates addressing the tradeoff between biological fidelity and model simplicity, and the reality of misspecification for models at all levels of complexity. We assess current methodological approaches to these issues via a case study of the 2010-2019 cholera epidemic in Haiti. We consider three dynamic models developed by expert teams to advise on vaccination policies. We evaluate previous methods used for fitting these models, and we demonstrate modified data analysis strategies leading to improved statistical fit. Specifically, we present approaches for diagnosing model misspecification and the consequent development of improved models. Additionally, we demonstrate the utility of recent advances in likelihood maximization for high-dimensional nonlinear dynamic models, enabling likelihood-based inference for spatiotemporal incidence data using this class of models. Our workflow is reproducible and extendable, facilitating future investigations of this disease system.

FOREWORD Finding Balance in the Fight Against Gun Violence.

The United States is distinct among high-income countries for its problem with gun violence, with Americans 25 times more likely to be killed by gun homicide than people in other high-income countries.1 Suicides make up a majority of annual gun deaths - though that gap is closing as homicides are on the rise - and the U.S. accounts for 35% of global firearm suicides despite making up only 4% of the world's population.2 More concerning, gun deaths are only getting worse. In 2021, firearm fatalities approached 50,000, the highest we have seen in at least 40 years.3 The increase in homicides in conjunction with lower crime overall further suggests an problem specifically with guns.4 As devastating as these deaths are, it does not come close to encompassing the mass toll of America's gun violence epidemic - a toll that disproportionately impacts people of color, with the Black community suffering at the highest rates. A broader and more accurate view of what constitutes gun violence must become a part of the national discourse if we are going to develop effective strategies to combat this crisis.5.

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At the end of 2022, the World Health Organization reported an increase in group A Streptococcus (GAS) infections, such as scarlet fever, in multiple countries. The outbreak primarily affected children under 10 years old, and the number of deaths was higher than anticipated, causing international concern. This paper reviews the current state of the GAS disease outbreak, its causes, and response measures. The authors aim to draw attention from clinical workers in China and increase their awareness and vigilance regarding this epidemic. Healthcare workers should be aware of the potential epidemiological changes in infectious diseases that may arise after the optimization of control measures for coronavirus disease 2019 to ensure children's health.

COVID-19 epidemic phases and morbidity in different areas of Chinese mainland, 2020.

Background: In the early stage of COVID-19 epidemic, the Chinese mainland once effectively controlled the epidemic, but COVID-19 eventually spread faster and faster in the world. The purpose of this study is to clarify the differences in the epidemic data of COVID-19 in different areas and phases in Chinese mainland in 2020, and to analyze the possible factors affecting the occurrence and development of the epidemic. Methods: We divided the Chinese mainland into areas I, I and III, and divided the epidemic process into phases I to IV: limited cases, accelerated increase, decelerated increase and containment phases. We also combined phases II and III as outbreak phase. The epidemic data included the duration of different phases, the numbers of confirmed cases, asymptomatic infections, and the proportion of imported cases from abroad. Results: In area I, II and III, only area I has a Phase I, and the Phase II and III of area I are longer. In Phase IV, there is a 17-day case clearing period in area I, while that in area II and III are 2 and 0 days, respectively. In phase III or the whole outbreak phase, the average daily increase of confirmed cases in area I was higher than that in areas II and III (P = 0.009 and P = 0.001 in phase III; P = 0.034 and P = 0.002 in the whole outbreak phase), and the average daily in-hospital cases were most in area I and least in area III (P = 0.000, P = 0.000, and P = 0.000 in phase III; P = 0.000, P = 0.000, and P = 0.009 in the whole outbreak phase). The average number of daily in-hospital COVID-19 cases in phase III was more than that in phase II in each area (P = 0.000, P = 0.000, and P = 0.001). In phase IV, from March 18, 2020 to January 1, 2021, the increase of confirmed cases in area III was higher than areas I and II (both P = 0.000), and the imported cases from abroad in Chinese mainland accounted for more than 55-61%. From June 16 to July 2, 2020, the number of new asymptomatic infections in area III was higher than that in area II (P = 0.000), while there was zero in area I. From July 3, 2020 to January 1, 2021, the increased COVID-19 cases in area III were 3534, while only 14 and 0, respectively, in areas I and II. Conclusions: The worst epidemic areas in Chinese mainland before March 18, 2020 and after June 15, 2020 were area I and area III, respectively, and area III had become the main battlefield for Chinese mainland to fight against imported epidemic since March 18, 2020. In Wuhan, human COVID-19 infection might occur before December 8, 2019, while the outbreak might occur before January 16 or even 10, 2020. Insufficient understanding of COVID-19 hindered the implementation of early effective isolation measures, leading to COVID-19 outbreak in Wuhan, and strict isolation measures were effective in controlling the epidemic. The import of foreign COVID-19 cases has made it difficult to control the epidemic of area III. When humans are once again faced with potentially infectious new diseases, it is appropriate to first and foremost take strict quarantine measures as soon as possible, and mutual cooperation between regions should be explored to combat the epidemic.

Perfil das notificações de dengue e febre chikungunya em Cabo Frio/Rj, 2014-2019 / Profile of dengue and chikungunya notifications in Cabo Frio/Rj, 2014-2019 / Perfil de las notificaciones de dengue y chikungunya en Cabo Frio/Rj, 2014-2019

Introdução: De acordo com a literatura científica, diagnósticos clínicos diferenciais de arboviroses representam uma dificuldade no que tange à dengue, na medida em que está no Brasil há muitos anos, o que acarreta em ser a arbovirose mais conhecida no país. As notificações de arboviroses se tornaram obrigatórias para inserção no SINAN, possibilitando a construção de perfis demográficos e o cálculo de incidências a partir de informações específicas para estas doenças. No que tange à dengue, a epidemia deste agravo ocorre no país desde 1986, evidenciando falhas na prevenção, relacionadas a aspectos socioeconômicos e ambientais. Objetivo: analisar perfis das notificações de dengue e febre de chikungunya dos casos notificados no município de Cabo Frio. Metodologia: Trata-se de estudo transversal e descritivo, com uso de dados secundários do SINAN referentes a casos de arboviroses no município de Cabo Frio/RJ. Foram observadas variáveis relacionadas ao sexo, escolaridade, raça/cor e critérios de confirmação, além do grau de completude. Resultados: Foram notificados 8.777 casos suspeitos de arboviroses, incluindo-se 1.367 notificações (15,57%) referentes à febre de chikungunya e 1.986 (22,63%), à dengue. Em relação ao desfecho, 1186 casos (51,45%) foram fechados como inconclusivos e 344 destes (14,92%) foram descartados como arboviroses. Dentre os inconclusivos, 943 (79,51%) eram referentes à notificação de dengue, idem para os 277 casos descartados (80,52%). Conclusão: Observou-se baixa taxa de completude nas fichas de notificação, explicada pelo baixo número de recursos humanos e pela insuficiente infraestrutura. Sugere-se a interação de diferentes profissionais e pesquisadores, facilitando a compreensão da complexa dinâmica das arboviroses em questão.

Introduction: According to the scientific literature, differential clinical diagnoses of arboviruses represent a difficulty with regard to dengue, as it has been present in Brazil for many years, which makes it the most well-known arbovirus in the country. Notifications of arboviruses became mandatory for inclusion in SINAN, enabling the construction of demographic profiles and the calculation of incidences based on specific information for these diseases. With regard to dengue, the epidemic of this disease has occurred in the country since 1986, showing failures in prevention, related to socioeconomic and environmental aspects. Objective: to analyze profiles of notifications of dengue and chikungunya fever of cases notified in the municipality of Cabo Frio. Methodology: This is a cross-sectional and descriptive study, using secondary data from SINAN regarding cases of arboviruses in the municipality of Cabo Frio/RJ. Variables related to sex, education, race/color and confirmation criteria were observed, in addition to the degree of completeness. Results: 8,777 suspected cases of arboviruses were reported, including 1,367 reports (15.57%) referring to chikungunya fever and 1,986 (22.63%) to dengue fever. Regarding the outcome, 1186 cases (51.45%) were closed as inconclusive and 344 of these (14.92%) were discarded as arboviruses. Among the inconclusive ones, 943 (79.51%) were related to dengue notification, the same for the 277 discarded cases (80.52%). Conclusion: A low completeness rate was observed in the notification forms, explained by the low number of human resources and insufficient infrastructure. It is suggested the interaction of different professionals and researchers, facilitating the understanding of the complex dynamics of the arboviruses in question.

Introducción: Según la literatura científica, los diagnósticos clínicos diferenciales de los arbovirus representan una dificultad con respecto al dengue, ya que está presente en Brasil desde hace muchos años, lo que lo convierte en el arbovirus más conocido en el país. Las notificaciones de arbovirus pasaron a ser obligatorias para su inclusión en el SINAN, lo que permitió la construcción de perfiles demográficos y el cálculo de incidencias a partir de información específica de estas enfermedades. Con respecto al dengue, la epidemia de esta enfermedad se presenta en el país desde 1986, mostrando fallas en la prevención, relacionadas con aspectos socioeconómicos y ambientales. Objetivo: analizar perfiles de notificaciones de dengue y fiebre chikungunya de los casos notificados en el municipio de Cabo Frio. Metodología: Se trata de un estudio transversal y descriptivo, utilizando datos secundarios del SINAN sobre casos de arbovirus en el municipio de Cabo Frio/RJ. Se observaron variables relacionadas con el sexo, escolaridad, raza/color y criterios de confirmación, además del grado de completitud. Resultados: se notificaron 8.777 casos sospechosos de arbovirus, de los cuales 1.367 (15,57%) se referían a fiebre chikungunya y 1.986 (22,63%) a dengue. En cuanto al resultado, 1186 casos (51,45%) se cerraron como no concluyentes y 344 de estos (14,92%) se descartaron como arbovirus. Entre los inconclusos, 943 (79,51%) estaban relacionados con la notificación de dengue, lo mismo para los 277 casos descartados (80,52%). Conclusión: Se observó un bajo índice de completitud en los formularios de notificación, explicado por el bajo número de recursos humanos y la infraestructura insuficiente. Se sugiere la interacción de diferentes profesionales e investigadores, facilitando la comprensión de la compleja dinámica de los arbovirus en cuestión.

Incidência de casos confirmados de dengue no Estado do Paraná Brasil nos anos de 2016 a 2021 / Incidence of confirmed dengue cases in the State of Paraná Brazil in the years of 2016 to 2021 / Incidencia de casos confirmados de dengue en el Estado de Paraná Brasil en los años 2016 a 2021

A dengue é uma doença dolorosa e debilitante transmitida por insetos da espécie Aedes aegypti. Ela é definida como uma doença viral que, nos últimos anos, se espalhou vertiginosamente por todas as regiões tropicais e subtropicais do planeta. Este estudo teve como objetivo identificar e discutir o número e a taxa de incidência de casos de dengue no estado do Paraná utilizando-se dos boletins emitidos por semana epidemiológica nos anos de 2016 a 2021, considerando a sazonalidade da doença. Também se objetivou debater a incidência por macrorregional, as possíveis causas de períodos epidêmicos e ações de combate vetorial para redução dos casos da patologia. Foram utilizados como fonte de informações o banco de dados da Dengue/SVS/SESA, por meio de informes técnicos, disponibilizados pelo portal online de Boletins da Dengue Paraná da Secretaria de Estado de Saúde do Paraná. Conclui-se que o ano epidemiológico de 2019/2020 foi o de maior incidência e os anos epidemiológicos 2016/2017 e 2017/2018 apresentaram os menores casos durante todo período analisado. Dessa forma, a vigilância epidemiológica é muito importante para avaliação espacial da distribuição de casos para execução de ações estratégicas para redução da infestação do vetor. As políticas públicas e a disponibilização de inseticidas para aplicação também são essenciais para o combate da Dengue.

Dengue is a painful and debilitating disease transmitted by insects of the Aedes aegypti species. It is defined as a viral disease that, in recent years, has spread vertiginously throughout the tropical and subtropical regions of the planet. This study aimed to identify and discuss the number and incidence rate of dengue cases in the state of Paraná using the bulletins issued by epidemiological week in the years 2016 to 2021, considering the seasonality of the disease. The aim was also to discuss the incidence per macro-region, the possible causes of epidemic periods, and vectorial combat actions to reduce the cases of the pathology. The Dengue/SVS/SESA database was used as a source of information, through technical reports, made available by the online portal of Dengue Paraná Bulletins of the Paraná State Health Department. It is concluded that the epidemiological year 2019/2020 was the one with the highest incidence and the epidemiological years 2016/2017 and 2017/2018 had the lowest cases during the entire period analyzed. Thus, epidemiological surveillance is very important for the spatial assessment of the distribution of cases to carry out strategic actions to reduce vector infestation. Public policies and the availability of insecticides for application are also essential to combat Dengue.

El dengue es una enfermedad dolorosa y debilitante transmitida por insectos de la especie Aedes aegypti. Se define como una enfermedad viral que, en los últimos años, se ha extendido vertiginosamente por las regiones tropicales y subtropicales del planeta. Este estudio tuvo como objetivo identificar y discutir el número y la tasa de incidencia de los casos de dengue en el estado de Paraná utilizando los boletines emitidos por la semana epidemiológica en los años 2016 a 2021, considerando la estacionalidad de la enfermedad. También se pretendía discutir la incidencia por macrorregiones, las posibles causas de los periodos epidémicos y las acciones de control de vectores para la reducción de los casos de la enfermedad. Se utilizó como fuente de información la base de datos de Dengue/SVS/SESA, por medio de informes técnicos, puestos a disposición por el portal online de Boletines de Dengue Paraná de la Secretaría de Salud del Estado de Paraná. Se concluye que el año epidemiológico 2019/2020 fue el de mayor incidencia y los años epidemiológicos 2016/2017 y 2017/2018 presentaron los menores casos durante todo el periodo analizado. Por lo tanto, la vigilancia epidemiológica es muy importante para la evaluación espacial de la distribución de los casos para la implementación de acciones estratégicas para reducir la infestación del vector. Las políticas públicas y la disponibilidad de insecticidas para su aplicación también son esenciales para combatir el dengue.

Effects of the information-driven awareness on epidemic spreading on multiplex networks.

In this study, we examine the impact of information-driven awareness on the spread of an epidemic from the perspective of resource allocation by comprehensively considering a series of realistic scenarios. A coupled awareness-resource-epidemic model on top of multiplex networks is proposed, and a Microscopic Markov Chain Approach is adopted to study the complex interplay among the processes. Through theoretical analysis, the infection density of the epidemic is predicted precisely, and an approximate epidemic threshold is derived. Combining both numerical calculations and extensive Monte Carlo simulations, the following conclusions are obtained. First, during a pandemic, the more active the resource support between individuals, the more effectively the disease can be controlled; that is, there is a smaller infection density and a larger epidemic threshold. Second, the disease can be better suppressed when individuals with small degrees are preferentially protected. In addition, there is a critical parameter of contact preference at which the effectiveness of disease control is the worst. Third, the inter-layer degree correlation has a "double-edged sword" effect on spreading dynamics. In other words, when there is a relatively lower infection rate, the epidemic threshold can be raised by increasing the positive correlation. By contrast, the infection density can be reduced by increasing the negative correlation. Finally, the infection density decreases when raising the relative weight of the global information, which indicates that global information about the epidemic state is more efficient for disease control than local information.

Epidemiology and Genetic Analysis of SARS-CoV-2 in Myanmar during the Community Outbreaks in 2020.

We aimed to analyze the situation of the first two epidemic waves in Myanmar using the publicly available daily situation of COVID-19 and whole-genome sequencing data of SARS-CoV-2. From March 23 to December 31, 2020, there were 33,917 confirmed cases and 741 deaths in Myanmar (case fatality rate of 2.18%). The first wave in Myanmar from March to July was linked to overseas travel, and then a second wave started from Rakhine State, a western border state, leading to the second wave spreading countrywide in Myanmar from August to December 2020. The estimated effective reproductive number (Rt) nationwide reached 6-8 at the beginning of each wave and gradually decreased as the epidemic spread to the community. The whole-genome analysis of 10 Myanmar SARS-CoV-2 strains together with 31 previously registered strains showed that the first wave was caused by GISAID clade O or PANGOLIN lineage B.6 and the second wave was changed to clade GH or lineage B.1.36.16 with a close genetic relationship with other South Asian strains. Constant monitoring of epidemiological situations combined with SARS-CoV-2 genome analysis is important for adjusting public health measures to mitigate the community transmissions of COVID-19.

Global Stability Analysis and Parameter Estimation for a Diphtheria Model: A Case Study of an Epidemic in Rohingya Refugee Camp in Bangladesh.

In this article, we have developed a deterministic Susceptible-Latent-Infectious-Recovered (SLIR) model for diphtheria outbreaks. Here, we have studied a case of the diphtheria outbreak in the Rohingya refugee camp in Bangladesh to trace the disease dynamics and find out the peak value of the infection. Both analytical and numerical investigations have been performed on the model to find several remarkable behaviors like the positive and bounded solution, basic reproductive ratio, and equilibria such as disease extinction equilibrium and disease persistence equilibrium which are characterized depending on the basic reproductive ratio and global stability of the model using Lyapunov function for both equilibria. Parameter estimation has been performed to determine the values of the parameter from the daily case data using numerical technique and determined the value of the basic reproductive number for the outbreak as ℛ 0 = 5.86.

Modalities and preferred routes of geographic spread of cholera from endemic areas in eastern Democratic Republic of the Congo.

Cholera is endemic along the Great Lakes Region, in eastern Democratic Republic of the Congo (DRC). From these endemic areas, also under perpetual conflicts, outbreaks spread to other areas. However, the main routes of propagation remain unclear. This research aimed to explore the modalities and likely main routes of geographic spread of cholera from endemic areas in eastern DRC. We used historical reconstruction of major outbreak expansions of cholera since its introduction in eastern DRC, maps of distribution and spatiotemporal cluster detection analyses of cholera data from passive surveillance (2000-2017) to describe the spread dynamics of cholera from eastern DRC. Four modalities of geographic spread and their likely main routes from the source areas of epidemics to other areas were identified: in endemic eastern provinces, and in non-endemic provinces of eastern, central and western DRC. Using non-parametric statistics, we found that the higher the number of conflict events reported in eastern DRC, the greater the geographic spread of cholera across the country. The present study revealed that the dynamics of the spread of cholera follow a fairly well-defined spatial logic and can therefore be predicted.

Model of Epidemic Kinetics with a Source on the Example of Moscow.

A new theoretical model of epidemic kinetics is considered, which uses elements of the physical model of the kinetics of the atomic level populations of an active laser medium as follows: a description of states and their populations, transition rates between states, an integral operator, and a source of influence. It is shown that to describe a long-term epidemic, it is necessary to use the concept of the source of infection. With a model constant source of infection, the epidemic, in terms of the number of actively infected people, goes to a stationary regime, which does not depend on the population size and the characteristics of quarantine measures. Statistics for Moscow daily increase in infected is used to determine the real source of infection. An interpretation of the waves generated by the source is given. It is shown that more accurate statistics of excess mortality can only be used to clarify the frequency rate of mortality of the epidemic, but not to determine the source of infection.

Novel Epidemic Metrics to Communicate Outbreak Risk at the Municipality Level: Dengue and Zika in the Dominican Republic.

Arboviruses remain a significant cause of morbidity, mortality and economic cost across the global human population. Epidemics of arboviral disease, such as Zika and dengue, also cause significant disruption to health services at local and national levels. This study examined 2014-2016 Zika and dengue epidemic data at the sub-national level to characterise transmission across the Dominican Republic. For each municipality, spatio-temporal mapping was used to characterise disease burden, while data were age and sex standardised to quantify burden distributions among the population. In separate analyses, time-ordered data were combined with the underlying disease migration interval distribution to produce a network of likely transmission chain events, displayed using transmission chain likelihood matrices. Finally, municipal-specific reproduction numbers (Rm) were established using a Wallinga-Teunis matrix. Dengue and Zika epidemics peaked during weeks 39-52 of 2015 and weeks 14-27 of 2016, respectively. At the provincial level, dengue attack rates were high in Hermanas Mirabal and San José de Ocoa (58.1 and 49.2 cases per 10,000 population, respectively), compared with the Zika burden, which was highest in Independencia and San José de Ocoa (21.2 and 13.4 cases per 10,000 population, respectively). Across municipalities, high disease burden was observed in Cotuí (622 dengue cases per 10,000 population) and Jimani (32 Zika cases per 10,000 population). Municipal infector-infectee transmission likelihood matrices identified seven 0% likelihood transmission events throughout the dengue epidemic and two 0% likelihood transmission events during the Zika epidemic. Municipality reproduction numbers (Rm) were consistently higher, and persisted for a greater duration, during the Zika epidemic (Rm = 1.0) than during the dengue epidemic (Rm < 1.0). This research highlights the importance of disease surveillance in land border municipalities as an early warning for infectious disease transmission. It also demonstrates that a high number of importation events are required to sustain transmission in endemic settings, and vice versa for newly emerged diseases. The inception of a novel epidemiological metric, Rm, reports transmission risk using standardised spatial units, and can be used to identify high transmission risk municipalities to better focus public health interventions for dengue, Zika and other infectious diseases.

VSHR: A Mathematical Model for the Prediction of Second-Wave COVID-19 Epidemics in Malaysia.

Since December 2019, a novel coronavirus (COVID-19) has spread all over the world, causing unpredictable economic losses and public fear. Although vaccines against this virus have been developed and administered for months, many countries still suffer from secondary COVID-19 infections, including the United Kingdom, France, and Malaysia. Observations of COVID-19 infections in the United Kingdom and France and their governance measures showed a certain number of similarities. A further investigation of these countries' COVID-19 transmission patterns suggested that when a turning point appeared, the values of their stringency indices per population density (PSI) were nearly proportional to their absolute infection rate (AIR). To justify our assumptions, we developed a mathematical model named VSHR to predict the COVID-19 turning point for Malaysia. VSHR was first trained on 30-day infection records prior to the United Kingdom, Germany, France, and Belgium's known turning points. It was then transferred to Malaysian COVID-19 data to predict this nation's turning point. Given the estimated AIR parameter values in 5 days, we were now able to locate the turning point's appearance on June 2nd, 2021. VSHR offered two improvements: (1) gathered countries into groups based on their SI patterns and (2) generated a model to identify the turning point for a target country within 5 days with 90% CI. Our research on COVID-19's turning point for a country is beneficial for governments and clinical systems against future COVID-19 infections.