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Each individual needs to be vaccinated to control the spread of the COVID-19 pandemic in the shortest possible time. However, the vaccine distribution with an already strained supply chain in low- and middle-income countries (LMICs) will not be effective enough to vaccinate all the population in stipulated time. The purpose of this paper is to show that there is a need to revolutionize the vaccine supply chain (VSC) by overcoming the challenges of sustainable vaccine distribution.

An integrated lean, agile and green (LAG) framework is proposed to overcome the challenges of the sustainable vaccine supply chain (SVSC). A hybrid best worst method (BWM)–Measurement of Alternatives and Ranking According to COmpromise Solution (MARCOS) methodology is designed to analyze the challenges and solutions.

The analysis shows that vaccine wastage is the most critical challenge for SVSC, and the coordination among stakeholders is the most significant solution followed by effective management support.

The result of the analysis can help the health care organizations (HCOs) to manage the VSC. The effective vaccination in stipulated time will help control the further spread of the virus, which will result in the normalcy of business and availability of livelihood for millions of people.

To the best of the author's knowledge, this is the first study to explore sustainability in VSC by considering the environmental and social impact of vaccination. The LAG-based framework is also a new approach in VSC to find the solution for existing challenges.

Participatory wireless sensor networks (PWSN) is an emerging paradigm that leverages existing sensing and communication infrastructures for the sensing task. Various environmental phenomenon – P monitoring applications dealing with noise pollution, road traffic, requiring spatio-temporal data samples of P (to capture its variations and its profile construction) in the region of interest – can be enabled using PWSN. Because of irregular distribution and uncontrollable mobility of people (with mobile phones), and their willingness to participate, complete spatio-temporal (CST) coverage of P may not be ensured. Therefore, unobserved data values must be estimated for CST profile construction of P and presented in this paper.

In this paper, the estimation of these missing data samples both in spatial and temporal dimension is being discussed, and the paper shows that non-parametric technique – Kernel Regression – provides better estimation compared to parametric regression techniques in PWSN context for spatial estimation. Furthermore, the preliminary results for estimation in temporal dimension have been provided. The deterministic and stochastic approaches toward estimation in the context of PWSN have also been discussed.

For the task of spatial profile reconstruction, it is shown that non-parametric estimation technique (kernel regression) gives a better estimation of the unobserved data points. In case of temporal estimation, few preliminary techniques have been studied and have shown that further investigations are required to find out best estimation technique(s) which may approximate the missing observations (temporally) with considerably less error.

This study addresses the environmental informatics issues related to deterministic and stochastic approaches using PWSN.