Modeling Uncertainties in Workforce Disruptions from Influenza Pandemics Using Dynamic Input‐Output Analysis

Haimar, Amine El; Santos, Joost R.

doi:10.1111/risa.12113

Cited by 8 publications

(5 citation statements)

References 27 publications

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“…An example of this would be the inoperability emanating from dysfunctionality of health-care service sectors. This explains the ranking of sectors S56 (social assistance) and S55 (health-care facilities) as the two most adversely impacted sectors in terms of inoperability level (El Haimar and Santos 2013). First, the personnel in these sectors come into a direct contact with the sick individuals; therefore, they have a much higher probability of contracting the influenza virus.…”

Section: Economic Loss Stabilizes In a Steady Statementioning

confidence: 98%

A stochastic recovery model of influenza pandemic effects on interdependent workforce systems

Haimar

Santos

2015

Nat Hazards

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Outbreaks of infectious diseases, such as pandemics, can result in adverse consequences and major economic losses across various economic sectors. Based on findings from the 2009 A H1N1 pandemic in the National Capital Region (NCR), this paper presents a recovery analysis for workforce disruptions using economic input-output modeling. The model formulation takes into consideration the dynamic interdependencies across sectors in an economic system in addition to the inherent characteristics of the economic sectors. From a macroeconomic perspective, the risk of the influenza disaster can be modeled using two risk metrics. First, there is the level of inoperability, which represents the percentage difference between the ideal production level and the degraded production level. Second, the economic loss metric represents the financial value associated with the reduced output. The contribution of this work revolves around the modeling of uncertainties triggered by new perturbations to interdependent economic sectors within an influenza pandemic timeline. We model the level of inoperability of economic sectors throughout their recovery horizon from the initial outbreak of the disaster using a dynamic model. Moreover, we use the level of inoperability values to quantify the cumulative economic losses incurred by the sectors within the recovery horizon. Finally, we revisit the 2009 NCR pandemic scenario to demonstrate the use of uncertainty analysis in modeling the inoperability and economic loss behaviors due to time-varying perturbations and their associated ripple effects to interdependent economic sectors.

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Section: Economic Loss Stabilizes In a Steady Statementioning

confidence: 98%

A stochastic recovery model of influenza pandemic effects on interdependent workforce systems

Haimar

Santos

2015

Nat Hazards

Self Cite

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“…Exploration of economic losses caused by the pandemic situation is a popular theme of research. Analyzing the data from the 2009 H1N1 pandemic, Haimar and Santos [38] identify that the sectors with higher production outputs experience a significant economic loss due to the pandemic. Fan et al [39] estimate the expected annual loss of USD 500 billion due to the global influenza pandemic.…”

Section: Literature Reviewmentioning

confidence: 99%

White knight in dark days? Supply chain finance firms, blockchain, and the COVID-19 pandemic

Paul¹,

Adhikari²,

Bose³

2022

Information & Management

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“…It is expected that this will have implications for associated sectors such as the food and drink sector, tourism sector, and event businesses. Haimes and Santos (2014)…”

Section: Introductionmentioning

confidence: 99%

A resilience analysis of the contraction of the accommodation and food service sector on the Scottish food industry

Revoredo‐Giha¹,

Dogbe²

2023

Front. Sustain. Food Syst.

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The Scottish economy, such as the United Kingdom (UK) economy, has been exposed to several adverse shocks over the past 5 years. Examples of these are the effect of the United Kingdom exiting the European Union (Brexit), the effects of the COVID-19 pandemic, and more recently Russia–Ukraine war, which can result in adverse direct and indirect economic losses across various sectors of the economy. These shocks disrupted the food and drink supply chains. The purpose of this article is 3-fold: (1) to explore the degree of resilience of the Scottish food and drink sector, (2) to estimate the effects on interconnected sectors of the economy, and (3) to estimate the economic losses, which is the financial value associated with the reduction in output. This article focuses on the impact that the sudden contraction that the “accommodation and food service activities”, resulting from the pandemic, had on the food and drink sectors. For this analysis, the study relied on the dynamic inoperability input–output model (DIIM), which takes into account the relationships across the different sectors of the Scottish economy over time. The results indicate that the accommodation and food service sector was the most affected by the COVID-19 pandemic lockdown contracting by approximately 60%. The DIIM shows that the disruption to this sector had a cascading effect on the remaining 17 sectors of the economy. The processed and preserved fish, fruits, and vegetable sector is the least resilient, while preserved meat and meat product sector is the most resilient to the final demand disruption in the accommodation and food service sector. The least economically affected sector was the other food product sector, while the other service sector had the highest economic loss. Although the soft drink sector had a slow recovery rate, economic losses were lower compared to the agricultural, fishery, and forestry sectors. From the policy perspective, stakeholders in the accommodation and food service sector should re-examine the sector and develop capacity against future pandemics. In addition, it is important for economic sectors to collaborate either vertically or horizontally by sharing information and risk to reduce the burden of future disruptions. Finally, the most vulnerable sectors of the economy, i.e., other service sectors should form a major part of government policy decision-making when planning against future pandemics.

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Modeling Uncertainties in Workforce Disruptions from Influenza Pandemics Using Dynamic Input‐Output Analysis

Cited by 8 publications

References 27 publications

A stochastic recovery model of influenza pandemic effects on interdependent workforce systems

A stochastic recovery model of influenza pandemic effects on interdependent workforce systems

White knight in dark days? Supply chain finance firms, blockchain, and the COVID-19 pandemic

A resilience analysis of the contraction of the accommodation and food service sector on the Scottish food industry

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