A Markov model of COVID-19 susceptibilities, infections, recoveries and fatalities: evidence from Nigeria

Purpose The purpose of this study is to determine the proportion of the population that will be susceptible to the COVID-19 pandemic, as well as the proportions of infections, recoveries and fatalities from the COVID-19 pandemic. Design/methodology/approach The design was a longitudinal survey of COVID-19 infections, recoveries and fatalities in Nigeria using the data on the daily updates of the Nigeria Centre for Disease Control for the period 1 May to 23 August 2020. Markov chain analysis was performed on the data. Findings The results showed that in the long run, 8.4% of the population will be susceptible to COVID-19 infections, 26.4% of them will be infected, 61.2% of the infected will recover and 4% will become fatal. Thus, if this pattern of infections and recoveries continue, the majority of the infected people in Nigeria will recover whilst a very small proportion of the infected people will die. Research limitations/implications A dearth of the extant literature on the problem, especially from the management science perspective. Practical implications Results of the study will facilitate policymakers’ response to the curtailment of the pandemic in Nigeria. Social implications Curtailing the pandemic through the results of this study will assist in easing the social consequences of the pandemic. Originality/value The proposed adjustment to the susceptibilities, infections and recoveries model through the introduction of a fourth state (fatality) to get the susceptibilities, infections, recoveries and fatalities model, signalling a point of departure from previous studies.

Structural Changes in Basmati Rice Exports from India: A Markov Chain Analysis

Basmati rice is considered a “strategic“ agricultural export commodity from India and has consequently remained subject to a wide range of government interventions. Basmati rice is exported from India to many countries, especially to the Gulf and European countries. A steady increase in Bbasmati rice production, availability of buffer stocks, and growing demand for basmati rice in the international market made India a vital rice exporting country of the world. The present study explored the structural changes in basmati rice exports from India from 1991 to 2020. The stability in the importing partners of basmati rice from India was analyzed using first-order Markov chain analysis. The compound annual growth rate for the export quantity of basmati rice varied from 5.74 to 12.65 percent per annum. In contrast, the export value of basmati rice ranged between 4.40 to 21.06 percent per annum during the three selected decades. Further, the basmati rice export value showed higher instability than export quantity. Saudi Arabia, Iran, and the United Arab Emirates served to be the stable importers. In contrast, the United States and the United Kingdom served as the most unstable markets for Indian basmati rice according to the transitional probability matrix. Therefore, to reduce variability in exports of basmati rice, India should maintain long-term stable trading partners further to increase the export earnings.

Stability Analysis of Australia’s Wool Exports- A Markov Chain Model

The present study is an attempt to analyse growth, instability and direction of trade of wool exports from Australia. Compound Annual Growth Rate, Cuddy-Della Valle Index and Markov chain analysis are the tools used for analysing data from 2008 to 2017. The growth rate of export in terms of quantity is negative and very low (-0.59% per annum) and the growth rate of exports in terms of value is positive (3.99% per annum). The instability index is low (2.78%) for exports in quantity terms and is medium (18.10%) for exports in value terms. China is the most stable market for export of wool from Australia with retention probability of 80.08%. The other reliable importers are Republic of Korea and Czech Republic. The study suggested the need to diversify Australian wool market.

Temporal Dynamics of Rice Production and Import in Nepal

Rice is a principal food crop for ensuring national food security and improving the livelihoods of the people in Nepal. To understand and analyse temporal dynamics of rice production and import, a study was carried out by using secondary data during the period from 2009 to 2018. The objectives of the study were to analyses temporal changes and dynamics of rice production and import in Nepal. The study used Compound growth rate and Markov chain analysis to assess the pattern of temporal changes in production and import of rice in Nepal for the last one decade. The synthesis of the analysis is presented in both tabular and graphic forms. The results revealed that Nepal is importing rice in increasing trend and not being able to meet national demands despite some increase in rice production and productivity in the last decade. Imports of rice were found in four harmonized system codes from 18 countries and the value of rice import is mounting in recent years. In addition, Compound growth rate analysis showed that the rice import quantity and value were increasing at the rate of 24.48 and 38.11 percent per annum respectively, while production growth was hovering below less than 2% per annum. The higher growth rate of value than quantity showed that mainly import is concentrated on fine and aromatic rice. Further, the study also reveals the direction of trade using Markov chain analysis, which shows that among selected countries, two countries India and the USA were more stable rice exporters to Nepal while China, Thailand, and Italy were less stable. The study implies that import substitution can be done by increasing productivity with increase use of high yielding hybrids and fine and aromatic rice.

Correlation of lithofacies and depositional environment using Markov chain analysis in Sambipitu formation at Ngalang river, Gunungkidul, Yogyakarta, Indonesia

The research area is located at Ngalang river, Gedangsari sub-district, Gunungkidul Regency, Special Region of Yogyakarta. The research area is part of southern mountain area which is composed of lithology of Sambipitu calcareous sandstone. The depositional process phase in the Sambipitu formation has a unique pattern and is relevant to the previous lithology. As a result, the stratigraphic position and lithological repetition pattern were reviewed using the statistical method (Markov chain). The aim of this research is to use geostatistics to examine the sedimentation trend in order to predict the existence of rock facies in the Sambipitu Formation. In each unit of lithology cycle, geostatistics is expected to assist, to predict and to interpret the significance of subsequent lithology appearances. The research method used was measured stratigraphy, determination of rock age and depositional environment based on fossil identification. In addition, this research used probability matrix in Markov chain analysis. The results of the Markov chain analysis showed that lithology of rock in the upper Sambipitu formation had a non-random transition pattern. The results of statistical calculation showed that the calculation value was greater than the Chi-square table value (333.9>34.38) that the H0 component was rejected. Lithofacies and depositional environment are correlated to several geological aspects such as distribution of rock facies, source of rock, paleobtahymetri, trace fossils and sedimentation process.

Export Competitiveness of Indian Mango: Determinants and Empirical Evidences

The main objective of the present study was to work out the export competitiveness of Indian mango and policy recommendations for improvement in its export and the destinations as well. To achieve the objectives, various tools such as net terms of the trade revealed comparative advantage, and Markov chain analysis was applied to mango export data from 1999-2019. The estimations of net terms of trade and revealed comparative advantage discovered that India has a comparative advantage for exporting mango. It is evident from the transitional probability matrices developed through Markov chain analysis that Qatar was the most loyal buyer for Indian mango during this period. The attention should be focused on the market requirement and specifications of Qatar, Bangladesh, UAE and the UK as they were the most stable buyers of Indian mango.