Spatio-Temporal Prediction of the Epidemic Spread of Dangerous Pathogens Using Machine Learning Methods

Real-time identification of the occurrence of dangerous pathogens is of crucial importance for the rapid execution of countermeasures. For this purpose, spatial and temporal predictions of the spread of such pathogens are indispensable. The R package papros developed by the authors offers an environment in which both spatial and temporal predictions can be made, based on local data using various deterministic, geostatistical regionalisation, and machine learning methods. The approach is presented using the example of a crops infection by fungal pathogens, which can substantially reduce the yield if not treated in good time. The situation is made more difficult by the fact that it is particularly difficult to predict the behaviour of wind-dispersed pathogens, such as powdery mildew (Blumeria graminis f. sp. tritici). To forecast pathogen development and spatial dispersal, a modelling process scheme was developed using the aforementioned R package, which combines regionalisation and machine learning techniques. It enables the prediction of the probability of yield- relevant infestation events for an entire federal state in northern Germany at a daily time scale. To run the models, weather and climate information are required, as is knowledge of the pathogen biology. Once fitted to the pathogen, only weather and climate information are necessary to predict such events, with an overall accuracy of 68% in the case of powdery mildew at a regional scale. Thereby, 91% of the observed powdery mildew events are predicted

Neural network based country wise risk prediction of COVID-19

The recent worldwide outbreak of the novel coronavirus (COVID-19) has opened up new challenges to the research community. Artificial intelligence (AI) driven methods can be useful to predict the parameters, risks, and effects of such an epidemic. Such predictions can be helpful to control and prevent the spread of such diseases. The main challenges of applying AI is the small volume of data and the uncertain nature. Here, we propose a shallow long short-term memory (LSTM) based neural network to predict the risk category of a country. We have used a Bayesian optimization framework to optimize and automatically design country-specific networks. The results show that the proposed pipeline outperforms state-of-the-art methods for data of 180 countries and can be a useful tool for such risk categorization. We have also experimented with the trend data and weather data combined for the prediction. The outcome shows that the weather does not have a significant role. The tool can be used to predict long-duration outbreak of such an epidemic such that we can take preventive steps earlie

Spatial data science

The field of data science has had a significant impact in both academia and industry, and with good reason [...]This research was partially funded by the Portuguese Foundation for Science and Technology (FCT),under projects IPSTERS (DSAIPA/AI/0100/2018), and foRESTER (PCIF/SSI/0102/2017)

When Infodemic Meets Epidemic: a Systematic Literature Review

Epidemics and outbreaks present arduous challenges requiring both individual and communal efforts. Social media offer significant amounts of data that can be leveraged for bio-surveillance. They also provide a platform to quickly and efficiently reach a sizeable percentage of the population, hence their potential impact on various aspects of epidemic mitigation. The general objective of this systematic literature review is to provide a methodical overview of the integration of social media in different epidemic-related contexts. Three research questions were conceptualized for this review, resulting in over 10000 publications collected in the first PRISMA stage, 129 of which were selected for inclusion. A thematic method-oriented synthesis was undertaken and identified 5 main themes related to social media enabled epidemic surveillance, misinformation management, and mental health. Findings uncover a need for more robust applications of the lessons learned from epidemic post-mortem documentation. A vast gap exists between retrospective analysis of epidemic management and result integration in prospective studies. Harnessing the full potential of social media in epidemic related tasks requires streamlining the results of epidemic forecasting, public opinion understanding and misinformation propagation, all while keeping abreast of potential mental health implications. Pro-active prevention has thus become vital for epidemic curtailment and containment

Modelling mechanisms of change in crop populations

Computer -based simulation models of changes occurring within crop populations when subjected to agents of phenotypic change, have been developed for use on commonly available personal computer equipment. As an underlying developmental principle, the models have been designed as general -case, mechanistic, stochastic models, in contrast to the predominantly empirically- derived, system -specific, deterministic (predictive) models currently available. A modelling methodology has evolved, to develop portable simulation models, written in high - level, general purpose code, allowing for use, modification and continued development by biologists with little requirement for computer programming expertise.The initial subject of these modelling activities was the simulation of the effects of selection and other agents of genetic change in crop populations, resulting in the computer model, PSELECT. Output from PSELECT, specifically phenotypic and genotypic response to phenotypic truncation selection, conformed to expectation, as defined by results from established analogue modelling work. Validation of the model by comparison of output with the results from an experimental -scale plant breeding exercise was less conclusive, and, owing to the fact that the genetic basis of the phenotypic characters used in the selection programme was insufficiently defined, the validation exercise provided only broad qualitative agreement with the model output. By virtue of the predominantly subjective nature of plant breeding programmes, the development of PSELECT resulted in a model of theoretical interest, but with little current practical application.Modelling techniques from the development of the PSELECT model were applied to the simulation of plant disease epidemics, where the modelled system is well characterised, and simulation modelling is an area of active research. The model SATSUMA, simulating the spatial and temporal development of diseases within crop populations, was developed. The model generates output which conforms to current epidemiological theory, and is compatible with contemporary methods of temporal and spatial analysis of crop disease epidemics. Temporal disease progress in the simulations was accurately described by variations of a generalised logistic model. Analysis of the spatial pattern of simulated epidemics by frequency distribution fitting or distance class methods was found to give good qualitative agreement with observed biological systems.The mechanistic nature of SATSUMA and its deliberate design as a general case model make it especially suitable for the investigation of component processes in a generalised plant disease epidemic, and valuable as an educational tool. Subject to validation against observational data, such models can be utilised as predictive tools by the incorporation of information (concerning crop species, pathogen etc.) specifically relevant to the modelled system. In addition to its educational use, SATSUMA has been used as research tool for the examination of the effect of spatial pattern of disease and disease incidence on the efficiency of sampling protocols and in parameterising a general theoretical model for describing the spatio -temporal development of plant diseases

Report on DIMACS Working Group Meeting: Mathematical Sciences Methods for the Study of Deliberate Releases of Biological Agents and their Consequences

55 pages, 1 article*Report on DIMACS Working Group Meeting: Mathematical Sciences Methods for the Study of Deliberate Releases of Biological Agents and their Consequences* (Castillo-Chavez, Carlos; Roberts, Fred S.) 55 page

Data mining techniques on satellite images for discovery of risk areas

The high rates of cholera epidemic mortality in less developed countries is a challenge for health fa- cilities to which it is necessary to equip itself with the epidemiological surveillance. To strengthen the capacity of epidemiological surveillance, this paper focuses on remote sensing satellite data processing using data mining methods to discover risk areas of the epidemic disease by connecting the environ- ment, climate and health. These satellite data are combined with field data collected during the same set of periods in order to explain and deduct the causes of the epidemic evolution from one period to another in relation to the environment. The existing technical (algorithms) for processing satellite im- ages are mature and efficient, so the challenge today is to provide the most suitable means allowing the best interpretation of obtained results. For that, we focus on supervised classification algorithm to process a set of satellite images from the same area but on different periods. A novel research method- ology (describing pre-treatment, data mining, and post-treatment) is proposed to ensure suitable means for transforming data, generating information and extracting knowledge. This methodology consists of six phases: (1.A) Acquisition of information from the field about epidemic, (1.B) Satellite data acquisition, (2) Selection and transformation of data (Data derived from images), (3) Remote sensing measurements, (4) Discretization of data, (5) Data treatment, and (6) Interpretation of results. The main contributions of the paper are: to establish the nature of links between the environment and the epidemic, and to highlight those risky environments when the public awareness of the problem and the prevention policies are absolutely necessary for mitigation of the propagation and emergence of the epidemic. This will allow national governments, local authorities and the public health officials to effective management according to risk areas. The case study concerns the knowledge discovery in databases related to risk areas of the cholera epidemic in Mopti region, Mali (West Africa). The results generate from data mining association rules indicate that the level of the Niger River in the wintering periods and some societal factors have an impact on the variation of cholera epidemic rate in Mopti town. More the river level is high, at 66% the rate of contamination is high

Public Sentiments towards the COVID-19 Pandemic: Insights from the Academic Literature Review and Twitter Analytics

The recent COVID-19 pandemic has severely impacted nations across the globe. Not only has it created economic shocks, but also long-term impacts on the social and psychological behaviors of the public. This can be attributed to the severity of the pandemic and because of the preventive and control measures such as global lockdowns, social distancing, and selfisolation that the governments imposed. Previous studies have reported significant changes in human emotions and behaviors are used to measure public sentiments about certain phenomena (such as the recent pandemic). The present study aims to study the public's sentiments during the COVID-19 outbreak based on an analytics review of public tweets highlighting changes in emotions. A dataset of 58,320 tweets extracted from Twitter and 61 academic articles was explored to analyze behavioral and emotional changes during previous and current pandemic situations. We chose the RPA – COV (Research Process Approach – COVID-19) approach, which was combined with the LBTA (Literature-Based Thematic Analysis) and the COVTA (COVID-19 Twitter Analytics). The sentiments' analysis results were coupled with word-tree analysis and highlighted that the public showed more highly neutral, positive, and mixed emotions than negative ones. The analysis pointed that people may react differently on Twitter as compared to real-life circumstances. The present study makes a significant contribution towards understanding how the public express their sentiments in pandemic situations

Spatio-temporal modeling of arthropod-borne zoonotic diseases: a proposed methodology to enhance macro-scale analyses

Zoonotic diseases are infectious diseases that can be transmitted from or through animals to humans, and arthropods often act as vectors for transmission. Emerging infectious diseases have been increasing both in prevalence and geographic range at alarming rates the last 30 years, and the majority of these diseases are zoonotic in nature. Many zoonotic diseases are considered notifiable by the Centers for Disease Control and Prevention (CDC). However, though state regulations or contractual obligations may require the reporting of certain diseases, significant underreporting is known to exist. Because of the rich volume of information captured in health insurance plan databases, administrative medical claims data could supplement the current reporting systems and allow for more comprehensive spatio-temporal analyses of zoonotic infections. The purpose of this dissertation is to introduce the use of electronic administrative medical claims data as a potential new source that could be leveraged in ecological field studies in the surveillance of arthropod-borne zoonotic diseases. If using medical claims data to study zoonoses is a viable approach, it could be used to improve both the temporal and spatial scale of study through the use of long-term longitudinal data covering large geographic expansions and more geographically refined ZIP code scales. Additionally, claims data could supplement the current reporting of notifiable diseases to the CDC. This effort may help bridge the disease incidence gap created by health care providers\u27 underreporting and thus allow for more effective tracking and monitoring of infectious zoonotic diseases across time and space. I specifically examined 5 tick-borne (Lyme disease [LD], babesiosis, ehrlichiosis, Rocky Mountain spotted fever [RMSF], and tularemia) and 2 mosquito-borne (West Nile virus, La Crosse viral encephalitis) diseases known to occur in the southeastern US. I first compared disease incidence rates from cases reported to the Tennessee Department of Health (TDH) state registry system with medically diagnosed cases captured in a southeastern managed care organization (MCO) claims data warehouse. I determined that LD and RMSF are significantly underreported in Tennessee. Three (3) cases of babesiosis were discovered in the claims data, a significant finding as this disease has never been reported in Tennessee. Next, I used a cluster scan statistic to statistically validate when (temporal) and where (spatial) these data sources differ. Findings highlight how the data sources do not overlap in their significant cluster results, supporting the need to integrate administrative and state registry data sources in order to provide a more comprehensive set of case information. Once the usefulness of administrative data was demonstrated, I focused on how these data could improve spatio-temporal macro-scale analyses by examining information at the ZIP code level as opposed to traditional county level assessments. I expanded on the current literature related to spatially explicit modeling by employing more advanced data mining modeling techniques. Four separate modeling techniques were compared (stepwise logistic regression, classification and regression tree, gradient boosted tree, and neural network) to describe the occurrence of tick-borne diseases as they relate to socio-demographic, geographic, and habitat characteristics. Covariates most useful in explaining LD and RMSF were similar and included co-occurrences of RMSF and LD, respectively, amount of forested and non-forested wetlands, pasture/grasslands, and urbanized/developed lands, population counts, and median income levels. Finally, I conclude with a ZIP code level spatio-temporal modeling exercise to determine areas and time periods in Tennessee where significant clusters of the studied diseases occurred. ZIP code level clusters were compared to the previously defined county-level clusters to discuss the importance of spatial scale. The findings suggest that focused disease/vector prevention efforts in non-endemic areas are warranted. Very little work exists using administrative claims data in the study of zoonotic diseases. This body of work thus adds to an area void of much knowledge. Administrative medical claims data are relatively easy to access given the appropriate permissions, have relatively no cost once access is granted, and provides the researcher with a volume rich dataset from which to study. This data source should be properly considered in the wildlife and biological sciences fields of research