Objectives From the beginning of the COVID-19 pandemic, clinical practice and research globally have centred on the prevention of transmission and treatment of the disease. The pandemic has had a huge impact on the economy and stressed healthcare systems worldwide. The present study estimates disability-adjusted life years (DALYs), years of potential productive life lost (YPPLL) and cost of productivity lost (CPL) due to premature mortality and absenteeism secondary to COVID-19 in the state of Kerala, India. Setting Details on sociodemographics, incidence, death, quarantine, recovery time, etc were derived from public sources and the Collective for Open Data Distribution-Keralam. The working proportion for 5-year age–gender cohorts and the corresponding life expectancy were obtained from the 2011 Census of India. Primary and secondary outcome measures The impact of the disease was computed through model-based analysis on various age–gender cohorts. Sensitivity analysis was conducted by adjusting six variables across 21 scenarios. We present two estimates, one until 15 November 2020 and later updated to 10 June 2021. Results Severity of infection and mortality were higher among the older cohorts, with men being more susceptible than women in most subgroups. DALYs for males and females were 15 954.5 and 8638.4 until 15 November 2020, and 83 853.0 and 56 628.3 until 10 June 2021. The corresponding YPPLL were 1323.57 and 612.31 until 15 November 2020, and 6993.04 and 3811.57 until 10 June 2021, and the CPL (premature mortality) were 263 780 579.94 and 41 836 001.82 until 15 November 2020, and 1 419 557 903.76 and 278 275 495.29 until 10 June 2021. Conclusions Most of the COVID-19 burden was contributed by years of life lost. Losses due to YPPLL were reduced as the impact of COVID-19 infection was lesser among the productive cohorts. The CPL values for individuals aged 40–49 years old were the highest. These estimates provide the data necessary for policymakers to work on reducing the economic burden of COVID-19 in Kerala.

Purpose: The paper aims to analyse the postharvest Supply Chain (SC) of tomatoes to maximise profit by restructuring the modes of operation.Design/methodology/approach: System Dynamics-based simulation of four scenarios depicting different operational modes of postharvest food SC was employed. Real-time of Tamil Nadu state, India was used to enact the scenarios using Vensim.Findings: Results indicated that cold storage improved the profit of wholesalers by prolonging the shelf-life of commodities. Retailers and farmers gained more profit in the absence of wholesalers. Though the absence of middlemen reduces the transit time and prevents deterioration, the role of wholesalers, i.e. transporting the commodities to farther customers’ needs to be shouldered by other agents effectively to minimise losses.Research limitations/implications: The accuracy of the results depend on the exactness of the data collected. The simulation findings, on the other hand, could be helpful in decision-making as these models portray the actual operational modes of postharvest SC. The suitability of each network structure depends on the capabilities of the agents, market scenario and demography. Implications based on discussions with stakeholders and in terms of establishing dedicated societies (cooperatives) have been provided.Originality/value: Postharvest losses incurred for horticultural crops like tomatoes are significantly high. It is of much importance to India as agriculture contributes to 17% of the Gross Domestic Product (GDP) and India is the second-largest producer of tomatoes globally. The study would shed light on restructuring the network appropriately.

Purpose: The textile industry contributes 2 and 3% to the global and Indian Gross Domestic Product (GDP), respectively. India supplies a quarter of global cotton yarn. Yet, most yarn manufacturing companies use outdated methods and lack organisational skills and strategies. Improvement in processes in India could significantly help the industry worldwide.Design/methodology/approach: The variables that influence the performance of the system were identified. Their interrelationships and impact were identified from the employees in the chosen case study, a yarn manufacturing industry. A System Dynamics (SD) approach was employed to study the benefits of implementing 5S lean strategies. The impact of each variable on various performance measures such as throughput, Work In Progress, processing time, waiting time, idle time, over-processing and scraps was analysed.Findings: Improvement in outcomes reflected an enhanced adoption of leanness in the industry. The decision-makers can utilise this study to optimise the necessary parameters in the system and attain the desired productivity levels. Better resource management and reduced processing time helped increase the despatch rate by 9.735% and decrease the WIP by 23.01%. Time management helped to reduce the inventory, idle time and waiting time. Over-processing, defects and scraps were minimised, indicating a shift towards lean.Research limitations/implications: This study pioneers the use of SD simulation models for optimising yarn manufacturing using lean strategies. Improvement in performance measures by integrating these strategies opens avenues for future research using multiple approaches to address a problem.Practical implications: Implementing 5S lean principles and simulations enhances productivity, reduces waste and optimises resource management for the yarn manufacturing industry. Decision-makers can employ simulation to witness the outcomes of their changes without investing cost and time and without associated implementation risks.Originality/value: The use of a simulation model to witness the benefits of incorporating lean strategies in yarn production has not been explored. This approach could help the managers and policymakers understand their existing system's shortcomings and critical areas that require improvement.

Objectives The COVID-19 pandemic has posed unprecedented challenges to health systems and populations, particularly in India. Comprehensive, population-level studies of the burden of disease could inform planning, preparedness and policy, but are lacking in India. In West Bengal, India, we conducted a detailed analysis of the burden caused by COVID-19 from its onset to 7 January 2022. Setting Open-access, population-level and administrative data sets for West Bengal were used. Primary and secondary outcome measures Disability-adjusted life years (DALYs), years of potential productive life lost (YPPLL), cost of productivity lost (CPL: premature mortality and absenteeism), years of potential life lost (YPLL), premature years of potential life lost, working years of potential life lost (WYPLL) and value of statistical life (VSL) were estimated across scenarios (21 for DALY and 3 each for YPLL and VSL) to evaluate the effects of different factors. Results COVID-19 had a higher impact on the elderly population with 90.2% of deaths arising from people aged above 45. In males and females, respectively, DALYs were 190 568.1 and 117 310.0 years, YPPLL of the productive population was 28 714.7 and 16 355.4 years, CPL due to premature mortality was INR3 198 259 615.6 and INR583 397 335.1 and CPL due to morbidity was INR2 505 568 048.4 and INR763 720 886.1. For males and females, YPLL ranged from 189 103.2 to 272 787.5 years and 117 925.5 to 169 712.0 years for lower to higher age limits, and WYPLL was 54 333.9 and 30 942.2 years. VSL (INR million) for the lower, midpoint and upper life expectancies was 883 330.8; 882 936.4; and 880 631.3, respectively. Vaccination was associated with reduced mortality. Conclusions The losses incurred due to COVID-19 in terms of the computed estimates in West Bengal revealed a disproportionately higher impact on the elderly and males. Analysis of various age-gender subgroups enhances localised and targeted policymaking to minimise the losses for future pandemics.

Purpose: Globalization and population explosion have worsened postharvest losses (PHL) in developing countries. This study looks to identify numerous controllable variables to reduce these losses and make the fresh produce supply chain more efficient. Design/methodology/approach: The study employs the interpretive structural modelling (ISM) technique to develop a hierarchical model to comprehend the intricate relationships between the variables influencing PHL. These variables are further classified based on the relative levels of importance in terms of their driving and dependence powers. Findings: The findings of this research provide variables for enterprises operating in fresh food supply chains to understand the specific risks that that supply chain faces and how these risks interact within the system. The fuzzy MICMAC analysis also classifies and highlights critical risk factors in the supply chain to aid implementation of PHL mitigation measures. The study highlights the importance of devising policies, legislation and efforts to regulate and curtail PHL across the global food supply chain. Research limitations/implications: The efficiency of the food supply chain contributes not only to economic sustainability but also to broader goals such as food security, better utilisation of global resources and sustainability in the supply chain. Social implications: It also highlights the significance of well-informed government policies, laws and regulations in successfully controlling and reducing PHL. Originality/value: This study compares factors contributing to PHL in the fresh produce supply chain and emphasises the stakeholders’ critical role in alleviating these losses. It also highlights the significance of well-informed government policies, laws and regulations in successfully controlling and reducing PHL.