Generic Approach For Mathematical Model of Multi-Strain Pandemics

Lazebnik, Teddy; Bunimovich‐Mendrazitsky, Svetlana

doi:10.1101/2021.11.16.468823

Cited by 5 publications

(5 citation statements)

References 54 publications

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“…In a complementary manner, the proposed work focused on the seeding strategy alone, ignoring current and novel possible monitoring and intervention policies strategies that can control a pandemic spread and therefore increase the economic profit [44,45]. In addition, as each plant type is susceptible to multiple pathogens, an extension of the proposed model for the case of a multi-strain pandemic can be of great interest [46,47,48,49,50].…”

Section: Discussionmentioning

confidence: 99%

Cost-optimal Seeding Strategy During a Botanical Pandemic in Domesticated Fields

Lazebnik¹

2023

Preprint

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Context: Botanical pandemics cause enormous economic damage and food shortage around the globe. However, since botanical pandemics are here to stay in the short-medium term, domesticated field owners can strategically seed their fields to optimize each session's economic profit.Objective: Given the pathogen's epidemiological properties, we aim to find an economically optimal grid-based seeding strategy for field owners and policymakers. Methods:We propose a novel epidemiological-economic mathematical model that describes the economic profit from a field of plants during a botanical pandemic. We describe the epidemiological dynamics using a spatiotemporal extended Susceptible-Infected-Recovered epidemiological model with a non-linear output epidemiological model. Results and Conclusions:We provide an algorithm to obtain an optimal grid-formed seeding strategy to maximize economic profit, given field and pathogen properties. In addition, we implement the proposed model in realistic settings, analyzing the sensitivity of the economic profit as a function of several epidemiological and economic properties. We show that the recovery and basic infection rates have a similar economic influence. Unintuitively, we show that in the context of a botanic pandemic, a larger farm does not promise higher economic profit.Significance: Our results demonstrate a significant benefit of using the proposed seeding strategy and shed more light on the dynamics of the botanical pandemic in domesticated fields.

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Section: Discussionmentioning

confidence: 99%

Cost-optimal Seeding Strategy During a Botanical Pandemic in Domesticated Fields

Lazebnik¹

2023

Preprint

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“…The social-epidemiological simulator is based on the classical SIR model proposed by [ 32 ] and extends it by introducing graph-based spatial dynamics and additional four epidemiological states. Moreover, we extended the SIR -type model to a SIRS -type model [ 33 , 34 ] which is known to better fit COVID-19 [ 35 , 36 ]. On top of this model, a social PIP which is based on mask-wearing, social distance, and vaccination PIPs is defined.…”

Section: Methodsmentioning

confidence: 99%

Early Detection and Control of the Next Epidemic Wave Using Health Communications: Development of an Artificial Intelligence-Based Tool and Its Validation on COVID-19 Data from the US

Lazebnik

Bunimovich‐Mendrazitsky

Ashkenazi

et al. 2022

IJERPH

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Social media networks highly influence on a broad range of global social life, especially in the context of a pandemic. We developed a mathematical model with a computational tool, called EMIT (Epidemic and Media Impact Tool), to detect and control pandemic waves, using mainly topics of relevance on social media networks and pandemic spread. Using EMIT, we analyzed health-related communications on social media networks for early prediction, detection, and control of an outbreak. EMIT is an artificial intelligence-based tool supporting health communication and policy makers decisions. Thus, EMIT, based on historical data, social media trends and disease spread, offers an predictive estimation of the influence of public health interventions such as social media-based communication campaigns. We have validated the EMIT mathematical model on real world data combining COVID-19 pandemic data in the US and social media data from Twitter. EMIT demonstrated a high level of performance in predicting the next epidemiological wave (AUC = 0.909, F1 = 0.899).

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“…While studies on the dynamics of two viral infections have considered cross-immunity and co-infection [28] , others described effects of two competing strains characterized by cross-immunity [29] . In fact, it has been reported that the increasing number of strains increases logarithmically the maximum number of infected individuals and the mean mortality rate [30] . Reinfection (potentially due to waning immunity) and multiple viral strains are some of the major challenges in the current COVID-19 pandemic [31] .…”

Section: Introductionmentioning

confidence: 99%