Parameter estimation and prediction for the course of a single epidemic outbreak of a plant disease.

Many epidemics of plant diseases are characterized by large variability among individual outbreaks. However, individual epidemics often follow a well-defined trajectory which is much more predictable in the short term than the ensemble (collection) of potential epidemics. In this paper, we introduce a modelling framework that allows us to deal with individual replicated outbreaks, based upon a Bayesian hierarchical analysis. Information about 'similar' replicate epidemics can be incorporated into a hierarchical model, allowing both ensemble and individual parameters to be estimated. The model is used to analyse the data from a replicated experiment involving spread of Rhizoctonia solani on radish in the presence or absence of a biocontrol agent, Trichoderma viride. The rate of primary (soil-to-plant) infection is found to be the most variable factor determining the final size of epidemics. Breakdown of biological control in some replicates results in high levels of primary infection and increased variability. The model can be used to predict new outbreaks of disease based upon knowledge from a 'library' of previous epidemics and partial information about the current outbreak. We show that forecasting improves significantly with knowledge about the history of a particular epidemic, whereas the precision of hindcasting to identify the past course of the epidemic is largely independent of detailed knowledge of the epidemic trajectory. The results have important consequences for parameter estimation, inference and prediction for emerging epidemic outbreaks.     

Bayesian data assimilation provides rapid decision support for vector-borne diseases

Predicting the spread of vector-borne diseases in response to incursions requires knowledge of both host and vector demographics in advance of an outbreak. Although host population data are typically available, for novel disease introductions there is a high chance of the pathogen using a vector for which data are unavailable. This presents a barrier to estimating the parameters of dynamical models representing host–vector–pathogen interaction, and hence limits their ability to provide quantitative risk forecasts. The Theileria orientalis (Ikeda) outbreak in New Zealand cattle demonstrates this problem: even though the vector has received extensive laboratory study, a high degree of uncertainty persists over its national demographic distribution. Addressing this, we develop a Bayesian data assimilation approach whereby indirect observations of vector activity inform a seasonal spatio-temporal risk surface within a stochastic epidemic model. We provide quantitative predictions for the future spread of the epidemic, quantifying uncertainty in the model parameters, case infection times and the disease status of undetected infections. Importantly, we demonstrate how our model learns sequentially as the epidemic unfolds and provide evidence for changing epidemic dynamics through time. Our approach therefore provides a significant advance in rapid decision support for novel vector-borne disease outbreaks.     

Spread of infectious disease through clustered populations

Networks of person-to-person contacts form the substrate along which infectious diseases spread. Most network-based studies of this spread focus on the impact of variations in degree (the number of contacts an individual has). However, other effects such as clustering, variations in infectiousness or susceptibility, or variations in closeness of contacts may play a significant role. We develop analytic techniques to predict how these effects alter the growth rate, probability and size of epidemics, and validate the predictions with a realistic social network. We find that (for a given degree distribution and average transmissibility) clustering is the dominant factor controlling the growth rate, heterogeneity in infectiousness is the dominant factor controlling the probability of an epidemic and heterogeneity in susceptibility is the dominant factor controlling the size of an epidemic. Edge weights (measuring closeness or duration of contacts) have impact only if correlations exist between different edges. Combined, these effects can play a minor role in reinforcing one another, with the impact of clustering the largest when the population is maximally heterogeneous or if the closer contacts are also strongly clustered. Our most significant contribution is a systematic way to address clustering in infectious disease models, and our results have a number of implications for the design of interventions.     

A dynamic,ensemble learning approach to forecast dengue fever epidemic years in Brazil using weather and population susceptibility cycles

Transmission of dengue fever depends on a complex interplay of human, climate and mosquito dynamics, which often change in time and space. It is well known that its disease dynamics are highly influenced by multiple factors including population susceptibility to infection as well as by microclimates: small-area climatic conditions which create environments favourable for the breeding and survival of mosquitoes. Here, we present a novel machine learning dengue forecasting approach, which, dynamically in time and space, identifies local patterns in weather and population susceptibility to make epidemic predictions at the city level in Brazil, months ahead of the occurrence of disease outbreaks. Weather-based predictions are improved when information on population susceptibility is incorporated, indicating that immunity is an important predictor neglected by most dengue forecast models. Given the generalizability of our methodology to any location or input data, it may prove valuable for public health decision-making aimed at mitigating the effects of seasonal dengue outbreaks in locations globally.     

Causes of delayed outbreak responses and their impacts on epidemic spread

Livestock diseases have devastating consequences economically, socially and politically across the globe. In certain systems, pathogens remain viable after host death, which enables residual transmissions from infected carcasses. Rapid culling and carcass disposal are well-established strategies for stamping out an outbreak and limiting its impact; however, wait-times for these procedures, i.e. response delays, are typically farm-specific and time-varying due to logistical constraints. Failing to incorporate variable response delays in epidemiological models may understate outbreak projections and mislead management decisions. We revisited the 2001 foot-and-mouth epidemic in the United Kingdom and sought to understand how misrepresented response delays can influence model predictions. Survival analysis identified farm size and control demand as key factors that impeded timely culling and disposal activities on individual farms. Using these factors in the context of an existing policy to predict local variation in response times significantly affected predictions at the national scale. Models that assumed fixed, timely responses grossly underestimated epidemic severity and its long-term consequences. As a result, this study demonstrates how general inclusion of response dynamics and recognition of partial controllability of interventions can help inform management priorities during epidemics of livestock diseases.     

A combined within-host and between-hosts modelling framework for the evolution of resistance to antimalarial drugs

The spread of drug resistance represents a significant challenge to many disease control efforts. The evolution of resistance is a complex process influenced by transmission dynamics between hosts as well as infection dynamics within these hosts. This study aims to investigate how these two processes combine to impact the evolution of resistance in malaria parasites. We introduce a stochastic modelling framework combining an epidemiological model of Plasmodium transmission and an explicit within-human infection model for two competing strains. Immunity, treatment and resistance costs are included in the within-host model. We show that the spread of resistance is generally less likely in areas of intense transmission, and therefore of increased competition between strains, an effect exacerbated when costs of resistance are higher. We also illustrate how treatment influences the spread of resistance, with a trade-off between slowing resistance and curbing disease incidence. We show that treatment coverage has a stronger impact on disease prevalence, whereas treatment efficacy primarily affects resistance spread, suggesting that coverage should constitute the primary focus of control efforts. Finally, we illustrate the importance of feedbacks between modelling scales. Overall, our results underline the importance of concomitantly modelling the evolution of resistance within and between hosts.     

A belief-based model for characterizing the spread of awareness and its impacts on individuals' vaccination decisions

During an epidemic, individuals'' decisions on whether or not to take vaccine may affect the dynamics of disease spread and, therefore, the effectiveness of disease control. Empirical studies have shown that such decisions can be subjected to individuals'' awareness about disease and vaccine, such as their perceived disease severity and vaccine safety. The aim of this paper is to gain a better understanding of individuals'' vaccination behaviour by modelling the spread of awareness in a group of socially connected individuals and examining the associated impacts on their vaccination decision-making. In our model, we examine whether or not individuals will get vaccinated as well as when they would. In doing so, we consider three possible decisions from an individual, i.e. to accept, to reject, and yet to decide, and further associate them with a set of belief values. Next, we extend the Dempster–Shafer theory to characterize individuals'' belief value updates and their decision-making, having incorporated the awareness obtained from their connected neighbours. Furthermore, we examine two factors that will affect individuals'' vaccination decisions: (i) reporting rates of disease- and vaccine-related events, and (ii) fading coefficient of awareness spread. By doing so, we can assess the impacts of awareness spread by evaluating the vaccination dynamics in terms of the number of vaccinated individuals. The results have demonstrated that the former influences the ratio of vaccinated individuals, whereas the latter affects the time when individuals decide to take vaccine.     

Inferring pandemic growth rates from sequence data

Using sequence data to infer population dynamics is playing an increasing role in the analysis of outbreaks. The most common methods in use, based on coalescent inference, have been widely used but not extensively tested against simulated epidemics. Here, we use simulated data to test the ability of both parametric and non-parametric methods for inference of effective population size (coded in the popular BEAST package) to reconstruct epidemic dynamics. We consider a range of simulations centred on scenarios considered plausible for pandemic influenza, but our conclusions are generic for any exponentially growing epidemic. We highlight systematic biases in non-parametric effective population size estimation. The most prominent such bias leads to the false inference of slowing of epidemic spread in the recent past even when the real epidemic is growing exponentially. We suggest some sampling strategies that could reduce (but not eliminate) some of the biases. Parametric methods can correct for these biases if the infected population size is large. We also explore how some poor sampling strategies (e.g. that over-represent epidemiologically linked clusters of cases) could dramatically exacerbate bias in an uncontrolled manner. Finally, we present a simple diagnostic indicator, based on coalescent density and which can easily be applied to reconstructed phylogenies, that identifies time-periods for which effective population size estimates are less likely to be biased. We illustrate this with an application to the 2009 H1N1 pandemic.     

Fluid fragmentation shapes rain-induced foliar disease transmission

Plant diseases represent a growing threat to the global food supply. The factors contributing to pathogen transmission from plant to plant remain poorly understood. Statistical correlations between rainfalls and plant disease outbreaks were reported; however, the detailed mechanisms linking the two were relegated to a black box. In this combined experimental and theoretical study, we focus on the impact dynamics of raindrops on infected leaves, one drop at a time. We find that the deposition range of most of the pathogen-bearing droplets is constrained by a hydrodynamical condition and we quantify the effect of leaf size and compliance on such constraint. Moreover, we identify and characterize two dominant fluid fragmentation scenarios as responsible for the dispersal of most pathogen-bearing droplets emitted from infected leaves: (i) the crescent-moon ejection is driven by the direct interaction between the impacting raindrop and the contaminated sessile drop and (ii) the inertial detachment is driven by the motion imparted to the leaf by the raindrop, leading to catapult-like droplet ejections. We find that at first, decreasing leaf size or increasing compliance reduces the range of pathogen-bearing droplets and the subsequent epidemic onset efficiency. However, this conclusion only applies for the crescent moon ejection. Above a certain compliance threshold a more effective mechanism of contaminated fluid ejection, the inertial detachment, emerges. This compliance threshold is determined by the ratio between the leaf velocity and the characteristic velocity of fluid fragmentation. The inertial detachment mechanism enhances the range of deposition of the larger contaminated droplets and suggests a change in epidemic onset pattern and a more efficient potential of infection of neighbouring plants. Dimensionless parameters and scaling laws are provided to rationalize our observations. Our results link for the first time the mechanical properties of foliage with the onset dynamics of foliar epidemics through the lens of fluid fragmentation. We discuss how the reported findings can inform the design of mitigation strategies acting at the early stage of a foliar disease outbreak.     

Antiviral treatment for the control of pandemic influenza: some logistical constraints.

Disease control programmes for an influenza pandemic will rely initially on the deployment of antiviral drugs such as Tamiflu, until a vaccine becomes available. However, such control programmes may be severely hampered by logistical constraints such as a finite stockpile of drugs and a limit on the distribution rate. We study the effects of such constraints using a compartmental modelling approach.We find that the most aggressive possible antiviral programme minimizes the final epidemic size, even if this should lead to premature stockpile run-out. Moreover, if the basic reproductive number R(0) is not too high, such a policy can avoid run-out altogether. However, where run-out would occur, such benefits must be weighed against the possibility of a higher epidemic peak than if a more conservative policy were followed.Where there is a maximum number of treatment courses that can be dispensed per day, reflecting a manpower limit on antiviral distribution, our results suggest that such a constraint is unlikely to have a significant impact (i.e. increasing the final epidemic size by more than 10%), as long as drug courses sufficient to treat at least 6% of the population can be dispensed per day.     

Buying Time for an Effective Epidemic Response: The Impact of a Public Holiday for Outbreak Control on COVID-19 Epidemic Spread

Rapid responses in the early stage of a new epidemic are crucial in outbreak control. Public holidays for outbreak control could provide a critical time window for a rapid rollout of social distancing and other control measures at a large population scale. The objective of our study was to explore the impact of the timing and duration of outbreak-control holidays on the coronavirus disease 2019 (COVID-19) epidemic spread during the early stage in China. We developed a compartment model to simulate the dynamic transmission of COVID-19 in China starting from January 2020. We projected and compared epidemic trajectories with and without an outbreak-control holiday that started during the Chinese Lunar New Year. We considered multiple scenarios of the outbreak-control holiday with different durations and starting times, and under different assumptions about viral transmission rates. We estimated the delays in days to reach certain thresholds of infections under different scenarios. Our results show that the outbreak-control holiday in China likely stalled the spread of COVID-19 for several days. The base case outbreak-control holiday (21 d for Hubei Province and 10 d for all other provinces) delayed the time to reach 100 000 confirmed infections by 7.54 d. A longer outbreak-control holiday would have had stronger effects. A nationwide outbreak-control holiday of 21 d would have delayed the time to 100 000 confirmed infections by nearly 10 d. Furthermore, we find that outbreak-control holidays that start earlier in the course of a new epidemic are more effective in stalling epidemic spread than later holidays and that additional control measures during the holidays can boost the holiday effect. In conclusion, an outbreak-control holiday can likely effectively delay the transmission of epidemics that spread through social contacts. The temporary delay in the epidemic trajectory buys time, which scientists can use to discover transmission routes and identify effective public health interventions and which governments can use to build physical infrastructure, organize medical supplies, and deploy human resources for long-term epidemic mitigation and control efforts.     

The spatial dissemination of COVID-19 and associated socio-economic consequences

The ongoing coronavirus disease 2019 (COVID-19) pandemic has wreaked havoc worldwide with millions of lives claimed, human travel restricted and economic development halted. Leveraging city-level mobility and case data, our analysis shows that the spatial dissemination of COVID-19 can be well explained by a local diffusion process in the mobility network rather than a global diffusion process, indicating the effectiveness of the implemented disease prevention and control measures. Based on the constructed case prediction model, it is estimated that there could be distinct social consequences if the COVID-19 outbreak happened in different areas. During the epidemic control period, human mobility experienced substantial reductions and the mobility network underwent remarkable local and global structural changes toward containing the spread of COVID-19. Our work has important implications for the mitigation of disease and the evaluation of the socio-economic consequences of COVID-19 on society.     

Modelling and Simulation of COVID-19 Outbreak Prediction Using Supervised Machine Learning

Novel Coronavirus-19 (COVID-19) is a newer type of coronavirus that has not been formally detected in humans. It is established that this disease often affects people of different age groups, particularly those with body disorders, blood pressure, diabetes, heart problems, or weakened immune systems. The epidemic of this infection has recently had a huge impact on people around the globe with rising mortality rates. Rising levels of mortality are attributed to their transmitting behavior through physical contact between humans. It is extremely necessary to monitor the transmission of the infection and also to anticipate the early stages of the disease in such a way that the appropriate timing of effective precautionary measures can be taken. The latest global coronavirus epidemic (COVID-19) has brought new challenges to the scientific community. Artificial Intelligence (AI)-motivated methodologies may be useful in predicting the conditions, consequences, and implications of such an outbreak. These forecasts may help to monitor and prevent the spread of these outbreaks. This article proposes a predictive framework incorporating Support Vector Machines (SVM) in the forecasting of a potential outbreak of COVID-19. The findings indicate that the suggested system outperforms cutting-edge approaches. The method could be used to predict the long-term spread of such an outbreak so that we can implement proactive measures in advance. The findings of the analyses indicate that the SVM forecasting framework outperformed the Neural Network methods in terms of accuracy and computational complexity. The proposed SVM system model exhibits 98.88% and 96.79% result in terms of accuracy during training and validation respectively.     

On spatially explicit models of cholera epidemics

We generalize a recently proposed model for cholera epidemics that accounts for local communities of susceptibles and infectives in a spatially explicit arrangement of nodes linked by networks having different topologies. The vehicle of infection (Vibrio cholerae) is transported through the network links that are thought of as hydrological connections among susceptible communities. The mathematical tools used are borrowed from general schemes of reactive transport on river networks acting as the environmental matrix for the circulation and mixing of waterborne pathogens. Using the diffusion approximation, we analytically derive the speed of propagation for travelling fronts of epidemics on regular lattices (either one-dimensional or two-dimensional) endowed with uniform population density. Power laws are found that relate the propagation speed to the diffusion coefficient and the basic reproduction number. We numerically obtain the related, slower speed of epidemic spreading for more complex, yet realistic river structures such as Peano networks and optimal channel networks. The analysis of the limit case of uniformly distributed population sizes proves instrumental in establishing the overall conditions for the relevance of spatially explicit models. To that extent, the ratio between spreading and disease outbreak time scales proves the crucial parameter. The relevance of our results lies in the major differences potentially arising between the predictions of spatially explicit models and traditional compartmental models of the susceptible–infected–recovered (SIR)-like type. Our results suggest that in many cases of real-life epidemiological interest, time scales of disease dynamics may trigger outbreaks that significantly depart from the predictions of compartmental models.     

Modelling the influence of human behaviour on the spread of infectious diseases: a review

Human behaviour plays an important role in the spread of infectious diseases, and understanding the influence of behaviour on the spread of diseases can be key to improving control efforts. While behavioural responses to the spread of a disease have often been reported anecdotally, there has been relatively little systematic investigation into how behavioural changes can affect disease dynamics. Mathematical models for the spread of infectious diseases are an important tool for investigating and quantifying such effects, not least because the spread of a disease among humans is not amenable to direct experimental study. Here, we review recent efforts to incorporate human behaviour into disease models, and propose that such models can be broadly classified according to the type and source of information which individuals are assumed to base their behaviour on, and according to the assumed effects of such behaviour. We highlight recent advances as well as gaps in our understanding of the interplay between infectious disease dynamics and human behaviour, and suggest what kind of data taking efforts would be helpful in filling these gaps.     

Attention-Based and Time Series Models for Short-Term Forecasting of COVID-19 Spread

The growing number of COVID-19 cases puts pressure on healthcare services and public institutions worldwide. The pandemic has brought much uncertainty to the global economy and the situation in general. Forecasting methods and modeling techniques are important tools for governments to manage critical situations caused by pandemics, which have negative impact on public health. The main purpose of this study is to obtain short-term forecasts of disease epidemiology that could be useful for policymakers and public institutions to make necessary short-term decisions. To evaluate the effectiveness of the proposed attention-based method combining certain data mining algorithms and the classical ARIMA model for short-term forecasts, data on the spread of the COVID-19 virus in Lithuania is used, the forecasts of epidemic dynamics were examined, and the results were presented in the study. Nevertheless, the approach presented might be applied to any country and other pandemic situations. The COVID-19 outbreak started at different times in different countries, hence some countries have a longer history of the disease with more historical data than others. The paper proposes a novel approach to data registration and machine learning-based analysis using data from attention-based countries for forecast validation to predict trends of the spread of COVID-19 and assess risks.     

Beyond R0: heterogeneity in secondary infections and probabilistic epidemic forecasting

The basic reproductive number, R₀, is one of the most common and most commonly misapplied numbers in public health. Often used to compare outbreaks and forecast pandemic risk, this single number belies the complexity that different epidemics can exhibit, even when they have the same R₀. Here, we reformulate and extend a classic result from random network theory to forecast the size of an epidemic using estimates of the distribution of secondary infections, leveraging both its average R₀ and the underlying heterogeneity. Importantly, epidemics with lower R₀ can be larger if they spread more homogeneously (and are therefore more robust to stochastic fluctuations). We illustrate the potential of this approach using different real epidemics with known estimates for R₀, heterogeneity and epidemic size in the absence of significant intervention. Further, we discuss the different ways in which this framework can be implemented in the data-scarce reality of emerging pathogens. Lastly, we demonstrate that without data on the heterogeneity in secondary infections for emerging infectious diseases like COVID-19 the uncertainty in outbreak size ranges dramatically. Taken together, our work highlights the critical need for contact tracing during emerging infectious disease outbreaks and the need to look beyond R₀.     

The challenges of COVID-19 control policies for sustainable development of business: Evidence from service industries

Such large-scale disruptions as the pandemic increase the uncertainty and risk related to business. Therefore, the business continuity management (BCM) has become an essential technical solution for enterprise emergency response. Since the beginning of 2020, the COVID-19 has spread worldwide at an alarming rate causing many threats to sustainable development of the business sector. The decline in consumer demand has hugely impacted service industries, such as wholesale and retail sales, tourism. Enterprise production and operations have faced severe challenges. In this study, we develop a risk factor analysis of BCM under the presence of COVID-19 in China. Based on a statistical survey of 940 enterprises in Hangzhou City, China, this study employs ordinal logistic regression to explore the hindering effect of risk factors introduced by the epidemic on business performance. Then, the interpretive structure model (ISM) is applied to analyze the hierarchical structure of the factors under examination. The key factors influencing the enterprise production and operation during COVID-19 outbreak significantly differ across the sub-sectors of the service industry. Therefore, this paper assesses the resilience of the productive technologies and business models of different industries amid the pandemic. This paper proposes epidemic prevention and control strategy focusing on investment and government regulation to ensure sustainable business development.     

Electron bunch compression and acceleration in the laser wakefield

The compression and acceleration of an external electron bunch into the laser wakefield is studied using 3D modeling with the LAPLAC code and compared to analytical predictions. It is shown, for a laser propagating in a plasma channel, that the nonlinear laser pulse dynamics together with the finite laser spot size influence the electron bunch compression and acceleration due to the reduction of the laser pulse group velocity. The transverse bunch dynamics and loading effect determine the final bunch charge and density and restrict the compressed sizes of the trapped and accelerated electron bunch. The dynamics of the electron bunch are illustrated with a set of parameters where the accelerated bunch acquires an energy of the order of 2 GeV, and 1% energy spread with sub-micron sizes.     

Incorporating individual health-protective decisions into disease transmission models: a mathematical framework

It is anticipated that the next generation of computational epidemic models will simulate both infectious disease transmission and dynamic human behaviour change. Individual agents within a simulation will not only infect one another, but will also have situational awareness and a decision algorithm that enables them to modify their behaviour. This paper develops such a model of behavioural response, presenting a mathematical interpretation of a well-known psychological model of individual decision making, the health belief model, suitable for incorporation within an agent-based disease-transmission model. We formalize the health belief model and demonstrate its application in modelling the prevalence of facemask use observed over the course of the 2003 Hong Kong SARS epidemic, a well-documented example of behaviour change in response to a disease outbreak.