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 Table of Contents  
ORIGINAL ARTICLE
Year : 2021  |  Volume : 8  |  Issue : 3  |  Page : 163-168

Correlates of COVID-19 incidence: A descriptive study


1 Department of Community Medicine, Bankura Sammilani Medical College, Bankura, West Bengal, India
2 Department of Community Medicine, ID and BG Hospital, Kolkata, West Bengal, India
3 Department of Community Medicine, Murshidabad Medical College, Berhampore, West Bengal, India
4 Department of Community Medicine, Medical College, Kolkata, West Bengal, India
5 Department of Pharmacology, Coochbehar Government Medical College, Cooch Behar, West Bengal, India
6 Department of Anatomy and Principal, B S Medical College, Bankura, West Bengal, India
7 Department of Onchopathology, Medical College, Kolkata, West Bengal, India

Date of Submission17-Jun-2020
Date of Decision02-Sep-2021
Date of Acceptance07-Sep-2021
Date of Web Publication04-Mar-2022

Correspondence Address:
Tanushree Mondal
Department of Community Medicine, Medical College, Kolkata, West Bengal
India
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/cjhr.cjhr_72_20

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  Abstract 


Background and Objectives: The enigma COVID-19 pandemic already involved major parts of globe with toll of 2,074,529 victims and 139,378 deaths from 213 countries/territories as on April 14, 2020. It cripples nations by the loss of human resources, economic decline, hunger, unemployment insecurities giving way to mental morbidities, and still many others to be discovered. Till it completes its trajectory, a systematic investigation, a prerequisite of any epidemic control, is warranted. Materials and Methods: A cross-sectional survey over 2 weeks (April 15, 2020–April 28, 2020) has been conducted at a teaching institution at Kolkata aiming to describe the magnitude, pattern, severity, and correlates of coronavirus pandemic 2020. Data pertaining to COVID-19 cases, deaths of affected countries, and their reported and or potential correlates were retrieved from various public domains, for example, https://www.who.int/emergencies/diseases/novel-coronavirus-2019/situation-reports, worldpopulationreview.com, data.worldbank.org. Results: Multiple linear regression analysis revealed a maximum R2 of 32.3% (P = 0.013) with a significant model fit (P = 0.000) for COVID-19 incidence rate per million which is associated positively with the proportion of the urban population (b = 0.024) and the percentage of the population aged 65 years or higher (b = 0.112) and negatively with current universal Bacille Calmette-Guérin vaccination (b = −1.021) policy of countries. Conclusion: Against this viral catastrophe evidence-based classical public health measures are underway. Notwithstanding variations in testing and reporting policy, the findings of this research ignite further study.

Keywords: Bacille Calmette-Guérin, COVID-19 pandemic, novel coronavirus, public health, urban population


How to cite this article:
Haldar D, Maji B, Ray SK, Mondal T, Adhikary A, Pradhan P, Burman DR. Correlates of COVID-19 incidence: A descriptive study. CHRISMED J Health Res 2021;8:163-8

How to cite this URL:
Haldar D, Maji B, Ray SK, Mondal T, Adhikary A, Pradhan P, Burman DR. Correlates of COVID-19 incidence: A descriptive study. CHRISMED J Health Res [serial online] 2021 [cited 2022 May 28];8:163-8. Available from: https://www.cjhr.org/text.asp?2021/8/3/163/339050




  Introduction Top


India as well as the whole nation is faced with a grim challenge that they have not encountered in the past few decades. It is the deadly attack of the novel coronavirus producing fatal pneumonia (COVID-19).

After being emerged in Wuhan, China in December 2019, the novel severe acute respiratory syndrome-coronavirus-2 coronavirus caused a large-scale COVID-19 pandemic and engulfed countries in its process culminating in one of the most deadly pandemics of the century. On April 14, 2020, the World Health Organization (WHO) reported 2,074,529 confirmed COVID-19 cases and 139,378 deaths from 213 countries/territories including India's contribution of 13,387 cases and 437 deaths.[1]

Lacking herd immunity and in the absence of effective vaccines or antiviral therapies, countries around the world are witnessing an unprecedented strain on health systems and disruption of economies as we start to understand the biology and mode of transmission of COVID-19.[2] As the entire world has been caught in the grip of the COVID-19 pandemic, a race to understand the virus and to find an effective and safe vaccine or treatment has resulted in the emergence of a number of studies. While expedient research about COVID-19 is currently of the utmost importance and urgency, it is crucial that analyses are based on sound statistical research that controls for confounding factors and that does not suffer from biases (e.g. omitted variable bias and self-selection bias).[3]

A pragmatic approach for finding out the influencing factors is a prerequisite of controlling any such devastating unknown pandemic. The present study was contemplated with the following objectives.

Objectives

  • To describe the pattern of the pandemic
  • To calculate country-wise COVID-19 incidence rate
  • To find out demographic, socioeconomic as well as climatic correlates, if any, of COVID-19 incidence.



  Materials and Methods Top


Design

A descriptive cross-sectional study was carried over 2 weeks (April 18–27, 2020) in a teaching Institute of Kolkata.

Population

All the COVID-19 cases and deaths from all the 213 affected countries/territories as on April 14, 2020.

Data sources

Data in relation to the COVID-19 pandemic were retrieved from the WHO's portal: https://www.who.int/emergencies/diseases/novel-coronavirus-2019/situation-reports. Various other data were availed from other public domains such as www.bcgatlas.org, worldpopulationreview.com, data.world bank.org, and www.worldometer.info.

Variables

Information pertaining to the name of affected countries/territories, subcontinent which they belong to, their situation in relation to the tropical region of the world; economic status (ES), total population, population density per km2, adult literacy rate (%), urban population (%), slum population (%), population engaged in agriculture (%), population aged 65 years or more (%), total labor force (k), total tourist arrived in the past year (k), and average annual temperature (°C) were retrieved from the above sources. Data relating to malaria transmission (intense, rare, and no), current Bacille Calmette-Guérin (BCG) vaccination strategy (single dose, multiple dose, and no use) of each of the affected countries were systematically gathered. Total number of country wise confirmed COVID-19 cases since January 21, 2020–April 17, 2020, nature of transmission in respective countries (sporadic, clustered, community, and pending) were obtained.

Based on per capita gross national income in current US$, Atlas method, the World Bank has classified the countries into:

  1. Low income (<1026)
  2. Lower-middle income (1026–3995)
  3. Upper-middle income (3996–12,375)
  4. High income (>12,375).


It has been adopted in this study. Continents are considered as per the WHO's categorization used in”novel coronavirus (2019 nCoV)” SITUATION REPORT from January 21, 2020. List of tropical countries was retrieved from the public domain “worldpopulationreview.com.”

Data were collected in a predesigned format. Throughout in this article not only the coronavirus pneumonia (which is actually designated as COVID-19) but also all diagnosed cases of coronavirus are loosely called COVID-19.

Statistical plan

Data were summarized by estimation crude rates, proportions, and displayed through charts and tables. The normality of variables was tested by the Kolmogorov–Smirnov test. Independent “t”-test, Mann–Whitney U test, Analysis of one way variance (ANOVA), Kruskal–Wallis ANOVA, and Pearson correlation coefficient (r) were used for bivariate analyses. Dummy variables were created for ES of countries having four levels before entering them into multiple linear regression models. International Business Machines (IBM), the Statistical package for social sciences (SPSS) version 22 (SPSS Inc., Chicago, Illinois, USA) was used for the data analysis. P < 0.05 was considered significant at 5% precision.

Ethical issues

The necessary approval from the concerned authority was sought for conducting this study.


  Results Top


All variables were found to have skewed distribution except the percentage of urban population. Then data set was tested for multivariate outliers by comparing the Mahalanobis distances to a Chi-square distribution using the degrees of freedom corresponding to the number of variables grouped together to calculate the Mahalanobis distances. No outlier with a Chi-square probability of <0.001 was detected. After log transforming, the dependent variables (DVs) i.e. incidence rate per million was found to become normally distributed (P = 0.200).

Epidemiological characteristics of COVID-19

As on April 14, 2020, the COVID-19 pandemic gripped altogether 213 countries/territories of which 34 (15.96%) were territories. Within 213 countries/territories COVID-19 transmission was categorized into sporadic (36.32%), cluster (27.83%), community (11.79%), and pending (24.06%). International conveyance (Diamond Princess) could not be categorized as country/territory.

Total 2,074,529 confirmed COVID-19 cases were reported from abovementioned countries/territories. Crude incidence per million people was estimated by dividing total cumulative COVID-9 cases by the estimated total population of the concerned countries.

Twelve countries/territories, namely, Yemen, São Tomé and Príncipe, South Sudan, Saint Pierre and Miquelon; Bonaire, Sint Eustatius and Saba, and Falkland Islands (Malvinas) were affected late and contributed only 25 COVID-19 cases and were not considered in subsequent analyses.

The euro-American region has lion's share of the burnt caused by the corona pandemic. Europe ranked on top by contributing 28.2% of affected countries, 50.7% incident cases followed by the American region with 25.4% victimized countries, and 35.8% of incidence. In contrast African part of the world though shared 22.1% of involved countries but contributed only 0.6% COVID-19 cases [Figure 1].
Figure 1: Distribution of COVID-19 cases and death according to subcontinents (up to April 14, 2020)

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A wide variation in COVID-19 incidence (per million) was observed within and between the subcontinents, for example, in the African region, Mayotte Versus Congo (858.8 vs. 1.3); in the Western Pacific region, North Macedonia Island versus Vietnam (19,654.6 vs. 2.8); in Europe, San Marino versus Uzbekistan (12,554.9 vs. 41.4); in the American region, Dominican Republic versus Venezuela (52162.9 vs. 6.9); in the Eastern Mediterranean region, Qatar versus Afghanistan (1430.6 vs. 21.9) and in SEAR, Thailand versus Sri Lanka (38.7 vs. 11.1), each country having minimum case of ≥100.

The Euro-American regions surpassed other subcontinents conspicuously by a sharp rising of pandemics observed on 7th and 8th week onward, respectively [Figure 2].
Figure 2: Distribution of subcontinents as per the trend of pandemic (up to April 14, 2020)

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China got full control over the situation within a span of 4 weeks. Small country Thailand could maintain mastery over the situation all along [Figure 3].
Figure 3: Time trend of COVID-19 epidemic in China, Thailand, Italy and India (up to April 21, 2020)

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The pandemic visited India on January 30, 2020, and awaited 2 months to take its hike typically started on March 31, 2020 [Figure 4].
Figure 4: Trend of COVID-19 epidemic in India in terms of weekly cases and deaths (up to April 21, 2020)

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As data regarding, the percentage of the population engaged in agriculture and percentage of urban population living in slum could not be accessed for a substantial number of affected countries, these two independent variables (IVs) were dropped from the final analysis.

Correlates of COVID-19 pandemic

Bivariate analyses showed that COVID-19 Incidence was significantly lower in countries with universal BCG vaccination, intense malaria transmission, and which are situated in the tropical region [Table 1].
Table 1: Relation between incidence of Coronavirus disease 2019 and few of its potential correlates

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ANOVA reflected that estimated COVID-19 incidence was low in upper-middle-income countries compared to that of high-income countries, lower in lower-middle countries compared to the upper-middle and high-income country, and lowest in low-income countries.

Further, bivariate analysis using the Pearson's correlation coefficient (r) revealed that DV Logincidence has a positive linear correlation with population density (r = 0.140, P = 0.047), proportion of urban (r = 0.171, P = 0.016) and aged population (r = 0.481, P = 0.000), literacy rate (r = 0.453, P = 0.000), ES (r = 0.584, P = 0.000), and negative relation with tropical position of country (r = −0.200, P = 0.004), universal BCG vaccination policy (r = −0.356, P = 0.000), and intense malaria transmission in the countries (r = −0.480, P = 0.000).

For delineating, the relative strength of each IV multiple linear regression analyses were carried out. Before entering into the model, IVs intense malaria transmission and universal BCG vaccination were dichotomized into attributes “present” or “absent” considering “rare” malaria transmission as “no” transmission and “multiple” BCG dose as “single” universal BCG coverage. Dummy variables, for example, ES1, ES2, and ES3 were created for IV ES.

For logincidence, original IVs found related with it in bivariate analyses were included in the regression model through the forward method and the model output revealed the highest R2 of 32.3% (significant 0.013) at a significant model fit (ANOVA [F3, 166 = 26.401, P = 0.000]) [[Table 2], model-3]. According to the model, the DV logincidence has a positive association with the percentage of aged and percentage of urban population and a negative relation with current universal BCG immunization policy of country. Collinearity statistics, i.e., tolerance >0.1 and variance inflation <10% are favorable toward model predictions.
Table 2: Results of multiple linear regressions through forward method (for logincidence)

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Interpretation of model output

Figure obtained by subtraction of 1.0 from the exponential value of beta coefficients multiplied by 100 gave the contribution of IVs in the variation of DV i.e. (exponential value of beta 1) ×100.[4] For example, beta-1.021 of BCG has its exponential value of 0.360. Subtraction of 1.0 from it ×100 gave a figure of-64.0%. It meant for every one unit increase in BCG vaccination would likely to reduce COVID-19 incidence by 0.64 units. Similarly, 12.0% and 2.0% of COVID-19 incidence would be more if 100 aged and urban residents were added, respectively [Table 2].


  Discussion Top


Wide variation in COVID-19 incidence as per this study might be due to differences in the genetic endowment of the virus/inhabitants and or local climatic, socio-cultural determinants altering disease dynamics.

Despite poverty, illiteracy, and inadequate health care; surprisingly, the African region is suffering less. It might due partly to less migration and less case detection in poor settings. However, in the later case, the possibility of more cases of severe acute respiratory infection/influenza such as illness and deaths arising out of them might have been reported [Figure 1].

Having sustained explosiveness in the epidemic with which the Western world is fighting now, China and small country Thailand were able to get control over the epidemic pragmatically. Euro-American countries definitely got a bit more time to respond [Figure 2] and [Figure 3].

Why did the China model fail to halt the disastrous march of the pandemic in Italy and other Euro-American countries is a query of the moment!

Having some illustrative measures to follow, the Government of India implemented evidence-based nonpharmaceutical interventions (NPIs) such as social distancing, cough etiquette, hand hygiene, quarantine, isolation, testing, and treatment of victims and now only time would say their timeliness and appropriateness.

The present study showed that countries with current universal BCG vaccination experienced less attack from COVID-19 compared to those without it. It might be due to immunemodulatory effect of BCG.

In concurrence with this finding, Hegarty et al. observed that countries with national programs for universal BCG vaccination appeared to have lower incidence and death rates from COVID-19. Similar to the observation of the present study, they also reported that countries that have a booster injection of BCG 7–14 years later had no better outcomes than those with a single inoculation only.[5]

Miller et al. reported that middle-high and high-income countries that have a current universal BCG strategy (55 countries) had 59.54 ± 23.29 (mean ± standard error of the mean) cases per million people. Consistent with a role of BCG in slowing the spread of COVID-19, middle-high and high-income countries that never had a universal BCG policy (5 countries) had about four times the number of cases per million inhabitants, with 264.90 ± 134.88. The between countries difference was robust (P = 0.0064, Wilcoxon rank-sum test), giving hints that universal BCG vaccination at early age along with other measures could slow the transmission of COVID-19.[6]

Singh et al. observed that mandatory BCG vaccination showed a highly significant (P < 0.0001) negative correlation with COVID-19 morbidity (r = −0.62) and mortality (r = −0.58) rates.[7]

However, Goswami et al. reported conflicting evidence that in Euro-American countries with low natural tubercular risk higher BCG coverage was associated with decreased COVID-19 incidence and mortality. On the contrary, increased BCG coverage appeared to have facilitated COVID-19 infection spread and mortality in the African, Asian, and Australasian continent having higher natural TB incidence. Now, an open and interesting question is whether, after adequate control of the spread of tuberculosis, the immunomodulatory effect of continued BCG vaccination, may help the human immune system to thwart future viral epidemics like the current COVID-19.[8]

Notwithstanding the ecological confounding, other studies also speculated about the protective role of BCG and proposed for trials to establish the unbiased role of BCG, if any.[9],[10]

Indeed, such trials are underway in the Netherlands, Australia, and Greece, while other trials are being planned in the United States, UK, Denmark, France, Uruguay, Tanzania, Uganda, and South Africa.[10]

According to the present study, COVID-19 incidence increased with the increasing proportion of aged people. Singh et al. observed the median age of the nation to have a significant (P < 0.0001) positive correlation with COVID-19 morbidity (r = 0.40).[7]

Santesmasses et al. found that the incidence of COVID-19 also grows exponentially with the median age of the country.[11]

Li et al. observed a relatively low incidence risk for young people but a very high mortality risk for seniors.[12]

The combined effect of age and comorbidity was not explored in this study. The extra risk imposed on elderly people is attributed to their propensity to comorbidities, for example, diabetes mellitus (DM), hypertension, coronary heart disease (CHD), cerebrovascular disease (CVD), chronic kidney disease, and malignancies. Indeed, these morbidities may start quite earlier than 65 years and play as an adjunct of growing age for making individuals easy prey to COVID-19.

In their study, Niu et al. observed high proportion of infected COVID-19 elderly had comorbidities, the most common being hypertension (48.8%), CHD (16.1%), chronic obstructive pulmonary disease (COPD [29.0%]), DM (9.7%), and CVD (6.5%) which has concordance with the previous study.[13],[14] Latest research reinstated that older patient and among those with coexisting conditions had higher morbidity and case fatality rate.[15]

A study done by Zhou et al. reported that 48% of the COVID-19 patients had comorbidity, with hypertension being the most common (30%), followed by DM (19%) and CHD (8%). Multivariable regression in their study showed increasing odds of hospital death associated with older age (odds ratio 1·10, 95% confidence interval 1·03–1·17, per year increase; P = 0·0043).[16]

Urban people are number of times more vulnerable to coronavirus infection. Specially, the urban poor without permanent job or working under the insecure condition in unorganized sectors, living in densely populated urban conglomeration and leading a poor quality of life is at stake. Wandering for jobs by public transport, lack of awareness, inaccessible health care owing to the structural inequality in the health system, and inability to adopt preventive measure, for example, wearing mask, washing hands, using alcohol-based sanitizer, and poor immune system makes them easy prey to coronavirus. As per urban health experts, busy western hubs share many similarities with Asian cities, whose crowded neighborhoods and slums are particularly vulnerable to disease outbreaks.[17]

Asian cities with footpath dwellers are at high risk of spreading the coronavirus infection. Pavement dwellers often double up as rag pickers and come in contact with wastes, for example, used masks, tissue paper, cotton, leftover medicine, or any other materials disposed by home-quarantined people.[18]

Limitation

This observational study is based on a single time-point data set with several confounding issues such as varied testing and reporting policy. Data pertaining to the age-sex structure of the country, nutritional status, religion, and ES of the individual patients could not be included for calculating specific attack rate. Data updating were varied for many countries. Estimated population was used. Missing data, as well as ecological confounding, are other issues.


  Conclusion Top


Till the full trajectory of COVID-19 emerges classic public health strategies, especially NPIs entangled with community participation are underway. Notwithstanding its constraints, present analyses provide intriguing observations that warrant large-scale collaborative research for finding out the answers for COVID-19. The study results will help the policymakers to design special comprehensive care for the people at risk.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.



 
  References Top

1.
Coronavirus Disease 2019 (COVID-19) Situation Report – 85. WHO; 2020 April 14. Available from: https://www.who.int/docs/default-source/coronaviruse/situation-reports/20200414-sitrep-85-covid-19.pdf?sfvrsn=7b8629bb. [Last accessed on 2020 Apr 15].  Back to cited text no. 1
    
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Ryan DH, Ravussin E, Heymsfield S. COVID 19 and the patient with obesity – The editors speak out. Obesity 2020;28:847.  Back to cited text no. 2
    
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Squalli J. Evaluating the determinants of COVID-19 mortality: A cross-country study. medRxiv 2020. doi: 10.1101/2020.05.12.20099093]. Available from: https://www.medrxiv.org/content/100.1101/2020.05.12.20099093v1.full.pdf. [Last accessed on 2020 May 18].  Back to cited text no. 3
    
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Ford C. Interpreting Log Transformations in a Linear Model; August, 201'8. Availed from: https://data.library.virginia.edu/interpreting-log-transformations-in-a-linear-model/. [Last accessed on 2020 Apr 14].  Back to cited text no. 4
    
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Hegarty PK, Kamat A, Zafirakis H, DiNardo A. BCG vaccination may be protective against COVID-19. 2020. [doi: 10.13140/RG.2.2.35948.10880]. Available from: https://www.researchgate.net/publication/340224580. [Last accessed on 2020 Apr 10].  Back to cited text no. 5
    
6.
Miller A, Reandelar MJ, Fasciglione K, Roumenova V, Li Y, Otazu GH. Correlation between universal BCG vaccination policy and reduced morbidity and mortality for COVID-19: An epidemiological study. medRxiv. [doi: 10.1101/2020.03.24.20042937].  Back to cited text no. 6
    
7.
Singh BR, Gandharva R, Karthikeyan R, Singh SV, Yadav A, Kumar Vinod OR, et al. Epidemiological determinants of acute respiratory syndrome coronavirus-2 disease pandemic and the role of the bacille-calmette-guerin vaccine in reducing morbidity and mortality. J Pure Appl Microbiol May 2020;14. Available from: https://www.researchgate.net/publication/340887162_Epidemiological_Determinants_of_Acute_Respiratory_Syndrome_Coronavirus-2_Disease_Pandemic_and_The_Role_of_the_Bacille-Calmette-Guerin_Vaccine_in_Reducing_Morbidity_and_Mortality_Epidemiological_Determ. [Last accessed on 2020 Apr 28].  Back to cited text no. 7
    
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Goswami RP, Mittal D, Goswami RP. Interaction between malarial transmission and BCG vaccination with COVID-19 incidence in the world map: A cross-sectional study. medRxiv. [doi: 10.1101/2020.04.03.20052563].  Back to cited text no. 8
    
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Ozdemir C, Kucuksezer UC, Tamay ZU. Is BCG vaccination affecting the spread and severity of COVID-19? Allergy 2020;75:1-3.  Back to cited text no. 9
    
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O'Neill LA, Netea MG. BCG-induced trained immunity: Can it offer protection against COVID-19? Nat Rev Immunol 2020;20:335-7.  Back to cited text no. 10
    
11.
Santesmasses D, Castro JP, Zenin AA, Shindyapina AV, Gerashchenko MV, Zhang B, et al. COVID-19 is an emergent disease of aging. medRxiv 2020. [doi: 10.1101/2020.04.15.20060095]. Availed from: https://www.medrxiv.org/content/100.1101/2020.04.15.20060095v1.full.pdf. [Last accessed on 2020 Jun 12].  Back to cited text no. 11
    
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Li H, Wang S, Zhong F, Bao W, Li Y, Liu L, et al. Age dependent risks of Incidence and Mortality of COVID 19 in Hubei Province and Other Parts of China. medRxiv 2020;1 13.[doi: 10.1101/2020.02.25.20027672]. Available at: https://www.medrxiv.org/content/10.1101/2020.02.25.20027672v2.full.pdf. [Last accessed on 2021 Sept. 12].  Back to cited text no. 12
    
13.
Niu S, Tian S, Lou J, Kang X, Zhang L, Lian H, et al. Clinical characteristics of older patients infected with COVID-19: A descriptive study. Arch Gerontol Geriatr 2020;89:104058.  Back to cited text no. 13
    
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Chen N, Zhou M, Dong X, Qu J, Gong F, Han Y, et al. Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: A descriptive study. Lancet 2020;395:507-13.  Back to cited text no. 14
    
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Peng YD, Meng K, Guan HQ, Leng L, Zhu RR, Wang BY, et al. Clinical characteristics and outcomes of 112 cardiovascular disease patients infected by 2019-nCoV. Zhonghua Xin Xue Guan Bing Za Zhi 2020;48:E004.  Back to cited text no. 15
    
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Zhou F, Yu T, Du R, Fan G, Liu Y, Liu Z, et al. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: A retrospective cohort study. Lancet 2020;395:1054-62.  Back to cited text no. 16
    
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Davies S. FEATURE-Poor city dwellers run greatest corona virus risk. In: Reuters. Barcelona: Thomson Reuters Foundation; 2020.  Back to cited text no. 17
    
18.
Basu J. COVID-19: Is Novel Coronavirus Spreading among Kolkata Pavement Dwellers through Waste. DownToEarth, Health; 2020, April 13. Available from: https://www.downtoearth.org.in/news/health/covid-19-is-novel-coronavirus-spreading-among-kolkata-pavement-dwellers-through-waste-70397. [Last accessed on 2020 Apr 14].  Back to cited text no. 18
    


    Figures

  [Figure 1], [Figure 2], [Figure 3], [Figure 4]
 
 
    Tables

  [Table 1], [Table 2]



 

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