The Early Blight of Tomato: Omics Interventions Toward Controlling Disease Spread and Development

In this article, a Susceptible – Vaccinated – Infected – Recovered (SVIR) model is formulated and analysed using comprehensive mathematical techniques. The vaccination class is primarily considered as means of controlling the disease spread. The basic reproduction number (Ro) of the model is obtained, where it was shown that if Ro<1, at the model equilibrium solutions when infection is present and absent, the infection- free equilibrium is both locally and globally asymptotically stable. Also, if Ro>1, the endemic equilibrium solution is locally asymptotically stable. Furthermore, the analytical solution of the model was carried out using the Differential Transform Method (DTM) and Runge - Kutta fourth-order method. Numerical simulations were carried out to validate the theoretical results.

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Development of the Biomaterials Technology for the Infection Resistance

Current Pharmaceutical Design ◽

10.2174/1381612824666180307121716 ◽

2018 ◽

Vol 24 (8) ◽

pp. 886-895 ◽

Cited By ~ 2

Author(s):

Tengzhou Xu ◽

Zhou Chen ◽

Zhaofeng Chen ◽

Yuxin Fan ◽

Haifeng Mao

Keyword(s):

Disease Spread ◽

Metallic Ions ◽

Comparative Performance ◽

Tissue Interactions ◽

Infection Resistance ◽

Glass Base ◽

Microbial Proliferation ◽

The Common ◽

Multicenter Clinical Trials ◽

Technical Solutions

Infections caused by microbial proliferation are one of the common issues and serious threats to the medical care, and they usually result in disease spread. Therefore, it is a significant issue for developing the antiinfective biomaterials to control this problem, according to the specific clinical application. Meanwhile, all their properties, the best anti-infective performance, the safe biocompatibility and the appropriate tissue interactions must be conformed to each other. At present, technologies are developing novel biomaterials and surfaces endowed with anti-infective properties, relying either on bactericidal or anti-biofilm activities. This review focuses on thoroughly summarizing numerous kinds of antibacterial biomaterials, including the antibacterial matrix biomaterials, antibacterial coatings and films, nanostructured materials and antibacterial fibers. Among these strategies, the utilization of bio-glass base and graphene base antibacterial matrix, and their effects on the antibiosis mechanism were emphatically discussed. Simultaneously, the effects and mechanisms of nano-coated metallic ions are also mentioned. Overall, there is a wealth of technical solutions to contrast the establishment of an implant infection. The lack of well-structured prospective multicenter clinical trials hinders the achievement of conclusive data on the efficacy and comparative performance of antibacterial biomaterials.

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COVID-19 Pandemic: Experiences in China and Implications for its Prevention and Treatment Worldwide

Current Cancer Drug Targets ◽

10.2174/1568009620666200414151419 ◽

2020 ◽

Vol 20 (6) ◽

pp. 410-416 ◽

Cited By ~ 6

Author(s):

Ning-Ning Liu ◽

Jing-Cong Tan ◽

Jingquan Li ◽

Shenghui Li ◽

Yong Cai ◽

...

Keyword(s):

Clinical Characteristics ◽

International Community ◽

Disease Spread ◽

Global Dynamics ◽

The World ◽

Prevention And Treatment ◽

History Of

The outbreak of COVID-19 due to SARS-CoV-2 originally emerged in Wuhan in December 2019. As of March 22, 2020, the disease spread to 186 countries, with at least 305,275 confirmed cases. Although there has been a decline in the spread of the disease in China, the prevalence of COVID-19 around the world remains serious despite containment efforts undertaken by national authorities and the international community. In this article, we systematically review the brief history of COVID-19 and its epidemic and clinical characteristics, highlighting the strategies used to control and prevent the disease in China, which may help other countries respond to the outbreak. This pandemic emphasizes the need to be constantly alert to shifts in both the global dynamics and the contexts of individual countries, making sure that all are aware of which approaches are successful for the prevention, containment and treatment of new diseases, and being flexible enough to adapt the responses accordingly.

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Understanding Economic and Health Factors Impacting the Spread of COVID-19 Disease (Preprint)

10.2196/preprints.19386 ◽

2020 ◽

Author(s):

Aleksandr Farseev ◽

Yu-Yi Chu-Farseeva ◽

Yang Qi ◽

Daron Benjamin Loo

Keyword(s):

Public Health ◽

Quantitative Research ◽

Health Indicators ◽

Initial Study ◽

Disease Spread ◽

Factors Affecting ◽

Country Level ◽

Development Scenarios ◽

Health Factors ◽

Country Clusters

UNSTRUCTURED The rapid spread of the Coronavirus 2019 disease (COVID-19) had drastically impacted life all over the world. While some economies are actively recovering from this pestilence, others are experiencing fast and consistent disease spread, compelling governments to impose social distancing measures that have put a halt on routines, especially in densely-populated areas. Aiming at bringing more light on key economic and public health factors affecting the disease spread, this initial study utilizes a quantitative statistical analysis based on the most recent publicly-available COVID-19 datasets. The study had shown and explained multiple significant relationships between the COVID-19 data and other country-level statistics. We have also identified and statistically profiled four major country-level clusters with relation to different aspects of COVID-19 development and country-level economic and health indicators. Specifically, this study has identified potential COVID-19 under-reporting traits as well as various economic factors that impact COVID-19 Diagnosis, Reporting, and Treatment. Based on the country clusters, we have also described the four disease development scenarios, which are tightly knit to country-level economic and public health factors. Finally, we have highlighted the potential limitation of reporting and measuring COVID-19 and provided recommendations on further in-depth quantitative research.

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PyDOSS: An open-source Python-based communicable disease outbreak surveillance system designed and used in a COVID-19 community care facility (Preprint)

10.2196/preprints.21760 ◽

2020 ◽

Author(s):

Andrew Fang ◽

Jonathan Kia-Sheng Phua ◽

Terrence Chiew ◽

Daniel De-Liang Loh ◽

Lincoln Ming Han Liow ◽

...

Keyword(s):

Open Source ◽

Surveillance System ◽

Community Care ◽

Communicable Disease ◽

Low Cost ◽

Real Life ◽

Disease Outbreak ◽

Disease Spread ◽

Healthcare Team ◽

Increased Risk

BACKGROUND During the Coronavirus Disease 2019 (COVID-19) outbreak, community care facilities (CCF) were set up as temporary out-of-hospital isolation facilities to contain the surge of cases in Singapore. Confined living spaces within CCFs posed an increased risk of communicable disease spread among residents. OBJECTIVE This inspired our healthcare team managing a CCF operation to design a low-cost communicable disease outbreak surveillance system (CDOSS). METHODS Our CDOSS was designed with the following considerations: (1) comprehensiveness, (2) efficiency through passive reconnoitering from electronic medical record (EMR) data, (3) ability to provide spatiotemporal insights, (4) low-cost and (5) ease of use. We used Python to develop a lightweight application – Python-based Communicable Disease Outbreak Surveillance System (PyDOSS) – that was able perform syndromic surveillance and fever monitoring. With minimal user actions, its data pipeline would generate daily control charts and geospatial heat maps of cases from raw EMR data and logged vital signs. PyDOSS was successfully implemented as part of our CCF workflow. We also simulated a gastroenteritis (GE) outbreak to test the effectiveness of the system. RESULTS PyDOSS was used throughout the entire duration of operation; the output was reviewed daily by senior management. No disease outbreaks were identified during our medical operation. In the simulated GE outbreak, PyDOSS was able to effectively detect an outbreak within 24 hours and provided information about cluster progression which could aid in contact tracing. The code for a stock version of PyDOSS has been made publicly available. CONCLUSIONS PyDOSS is an effective surveillance system which was successfully implemented in a real-life medical operation. With the system developed using open-source technology and the code made freely available, it significantly reduces the cost of developing and operating CDOSS and may be useful for similar temporary medical operations, or in resource-limited settings.

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The case for tracking misinformation the way we track disease

Big Data & Society ◽

10.1177/20539517211013867 ◽

2021 ◽

Vol 8 (1) ◽

pp. 205395172110138

Author(s):

Erika Bonnevie ◽

Jennifer Sittig ◽

Joe Smyser

Keyword(s):

Public Health ◽

Real Time ◽

Supervised Machine Learning ◽

Disease Spread ◽

The Public ◽

Monitoring Tools ◽

Core Function ◽

Public Health Organizations ◽

Front Line Workers ◽

Media Data

While public health organizations can detect disease spread, few can monitor and respond to real-time misinformation. Misinformation risks the public’s health, the credibility of institutions, and the safety of experts and front-line workers. Big Data, and specifically publicly available media data, can play a significant role in understanding and responding to misinformation. The Public Good Projects uses supervised machine learning to aggregate and code millions of conversations relating to vaccines and the COVID-19 pandemic broadly, in real-time. Public health researchers supervise this process daily, and provide insights to practitioners across a range of disciplines. Through this work, we have gleaned three lessons to address misinformation. (1) Sources of vaccine misinformation are known; there is a need to operationalize learnings and engage the pro-vaccination majority in debunking vaccine-related misinformation. (2) Existing systems can identify and track threats against health experts and institutions, which have been subject to unprecedented harassment. This supports their safety and helps prevent the further erosion of trust in public institutions. (3) Responses to misinformation should draw from cross-sector crisis management best practices and address coordination gaps. Real-time monitoring and addressing misinformation should be a core function of public health, and public health should be a core use case for data scientists developing monitoring tools. The tools to accomplish these tasks are available; it remains up to us to prioritize them.

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An analysis of COVID-19 clusters in India

BMC Public Health ◽

10.1186/s12889-021-10491-8 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Pooja Sengupta ◽

Bhaswati Ganguli ◽

Sugata SenRoy ◽

Aditya Chatterjee

Keyword(s):

Public Health ◽

Cluster Analysis ◽

Population Density ◽

Case Studies ◽

Compartment Model ◽

Health Interventions ◽

Disease Spread ◽

Second Phase ◽

Public Health Interventions ◽

Homogeneous Groups

Abstract Background In this study we cluster the districts of India in terms of the spread of COVID-19 and related variables such as population density and the number of specialty hospitals. Simulation using a compartment model is used to provide insight into differences in response to public health interventions. Two case studies of interest from Nizamuddin and Dharavi provide contrasting pictures of the success in curbing spread. Methods A cluster analysis of the worst affected districts in India provides insight about the similarities between them. The effects of public health interventions in flattening the curve in their respective states is studied using the individual contact SEIQHRF model, a stochastic individual compartment model which simulates disease prevalence in the susceptible, infected, recovered and fatal compartments. Results The clustering of hotspot districts provide homogeneous groups that can be discriminated in terms of number of cases and related covariates. The cluster analysis reveal that the distribution of number of COVID-19 hospitals in the districts does not correlate with the distribution of confirmed COVID-19 cases. From the SEIQHRF model for Nizamuddin we observe in the second phase the number of infected individuals had seen a multitudinous increase in the states where Nizamuddin attendees returned, increasing the risk of the disease spread. However, the simulations reveal that implementing administrative interventions, flatten the curve. In Dharavi, through tracing, tracking, testing and treating, massive breakout of COVID-19 was brought under control. Conclusions The cluster analysis performed on the districts reveal homogeneous groups of districts that can be ranked based on the burden placed on the healthcare system in terms of number of confirmed cases, population density and number of hospitals dedicated to COVID-19 treatment. The study rounds up with two important case studies on Nizamuddin basti and Dharavi to illustrate the growth curve of COVID-19 in two very densely populated regions in India. In the case of Nizamuddin, the study showed that there was a manifold increase in the risk of infection. In contrast it is seen that there was a rapid decline in the number of cases in Dharavi within a span of about one month.

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Identification of effective spreaders in contact networks using dynamical influence

Applied Network Science ◽

10.1007/s41109-021-00351-0 ◽

2021 ◽

Vol 6 (1) ◽

Author(s):

Ruaridh A. Clark ◽

Malcolm Macdonald

Keyword(s):

Disease Transmission ◽

Average Rate ◽

Laplacian Matrix ◽

Disease Spread ◽

Eigenvector Centrality ◽

Contact Networks ◽

Human Contact ◽

Spread Dynamics ◽

Definition Of ◽

High Degree

AbstractContact networks provide insights on disease spread due to the duration of close proximity interactions. For systems governed by consensus dynamics, network structure is key to optimising the spread of information. For disease spread over contact networks, the structure would be expected to be similarly influential. However, metrics that are essentially agnostic to the network’s structure, such as weighted degree (strength) centrality and its variants, perform near-optimally in selecting effective spreaders. These degree-based metrics outperform eigenvector centrality, despite disease spread over a network being a random walk process. This paper improves eigenvector-based spreader selection by introducing the non-linear relationship between contact time and the probability of disease transmission into the assessment of network dynamics. This approximation of disease spread dynamics is achieved by altering the Laplacian matrix, which in turn highlights why nodes with a high degree are such influential disease spreaders. From this approach, a trichotomy emerges on the definition of an effective spreader where, for susceptible-infected simulations, eigenvector-based selections can either optimise the initial rate of infection, the average rate of infection, or produce the fastest time to full infection of the network. Simulated and real-world human contact networks are examined, with insights also drawn on the effective adaptation of ant colony contact networks to reduce pathogen spread and protect the queen ant.

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Livestock movement informs the risk of disease spread in traditional production systems in East Africa

Scientific Reports ◽

10.1038/s41598-021-95706-z ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Divine Ekwem ◽

Thomas A. Morrison ◽

Richard Reeve ◽

Jessica Enright ◽

Joram Buza ◽

...

Keyword(s):

Disease Transmission ◽

Production Systems ◽

Control Strategies ◽

Herd Size ◽

Transmission Risk ◽

Disease Spread ◽

Transmission Potential ◽

Spatial And Temporal Scales ◽

Transmission Distance ◽

Risk Of Disease

AbstractIn Africa, livestock are important to local and national economies, but their productivity is constrained by infectious diseases. Comprehensive information on livestock movements and contacts is required to devise appropriate disease control strategies; yet, understanding contact risk in systems where herds mix extensively, and where different pathogens can be transmitted at different spatial and temporal scales, remains a major challenge. We deployed Global Positioning System collars on cattle in 52 herds in a traditional agropastoral system in western Serengeti, Tanzania, to understand fine-scale movements and between-herd contacts, and to identify locations of greatest interaction between herds. We examined contact across spatiotemporal scales relevant to different disease transmission scenarios. Daily cattle movements increased with herd size and rainfall. Generally, contact between herds was greatest away from households, during periods with low rainfall and in locations close to dipping points. We demonstrate how movements and contacts affect the risk of disease spread. For example, transmission risk is relatively sensitive to the survival time of different pathogens in the environment, and less sensitive to transmission distance, at least over the range of the spatiotemporal definitions of contacts that we explored. We identify times and locations of greatest disease transmission potential and that could be targeted through tailored control strategies.

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