Health

COVID-19: Trends show probable impact on low- and middle-income countries

Having the highest share of population and living in poor socio-economic environments, the spread of COVID-19 is easier in LMICs than developed countries

 
Aerial view of Zambia capital Lusaka. What makes this pandemic particularly difficult for countries like Zambia are structural constraints that already exist in their economies Photo: Wikimedia Commons

The novel coronavirus disease (COVID-19) pandemic is a public health threat globally, exerting a devastating impact on patients, healthcare providers, systems and financing.

Both developed and developing countries are struggling to control the pandemic, though the situation is alarming in low- and middle-income countries (LMICs).

Lack of social-distancing, higher population, health inequalities and an inadequate health infrastructure places a tremendous challenge to control COVID-19.

This study was undertaken to forecast the trends in outbreak of COVID-19 in LMICs (40 countries) based on the publicly available case data. An auto regressive integrated moving averages (ARIMA) model was used to predict trends in total confirmed and death cases caused by COVID-19.

Keeping in view the limited healthcare resources in LMICs, accurate forecasting and detection, stronger disease surveillance, and avoidance of acute-care for infected-cases is indispensable.

Over the past two decades, a large number of individuals and animals were affected by three epidemics caused by the family of coronaviruses. They were the Severe Acute Respiratory Syndrome (SARS) in 2003, the Middle East Respiratory Syndrome (MERS) in 2012 and COVID-19.

Significant genetic dissimilarities, however, were documented between pathogens of these three epidemics, particularly amid MERS and COVID-19.

Initially, the hotspots for these epidemics were west Asia, Saudi Arabia and China. They transmitted from animal to human and later transmissions of pathogens were reported from humans to humans in other countries as well.

Epidemiological evidence of the COVID-19 outbreak emerged from China’s Wuhan from December 12, 2019.

The World Health Organization declared COVID-19 a pandemic on March 11, 2020 as more than 118,000 cases were reported in 110 countries with a sustained worldwide risk of further spread.

According to estimates by the Imperial College, London, UK, in the absence of interventions, COVID-19 would have resulted in seven billion infections and 40 million deaths in 2020 globally. As of May 5, 2020, the spread of COVID-19 since the first case in December 2019, reached 3,582,469 confirmed cases including 2,51,365 deaths globally.

It is argued that developed countries have greater expertise in the investigation and management of such cases than LMICs.

Controlling spread in these countries would be critical as these LMICs accommodate about 6.3 billion people. The COVID-19 pandemic continues to create an acute shortage of essential supplies, personal protective equipment, diagnostics and medical supplies in LMICs.

The confirmed cases of COVID-19 are growing exponentially, according to real-time data. Therefore, to contain the spread of COVID-19, it is important to forecast the precise increase in the expected number of cases to comprehend what is required to control perturbing trends of the pandemic.

The accuracy in predictions will play a critical role in managing the health emergency and preparedness of the respective governments of these LMICs to tackle the situation.

We aim to assess the spread of COVID-19 in the LMICs from publicly available data and to assess the quality of official case records trends by using the ARIMA method. We assume that publicly available data of total confirmed cases and deaths is a legitimate subject to certain reporting biases, if any.

Material and methods

The data were collected from publicly available sources, including Our World in Data, on the total confirmed and death cases. The data were collected and analysed from December 31, 2019 to June 9, 2020. We used the ARIMA forecasting model, a popular statistical method for time-series forecasting.

In this method, we use weighted averages of past observations to forecast new values. Here, the idea is to give more importance to recent values in the series. Thus, as observations get older in time, the importance of these values gets exponentially smaller. A univariate time-series analysis method was used to model and forecast daily confirmed COVID-cases and death cases in LMICs.

Information was available from 40 LMICs, which was used in this study. The regressive forecast curves were consistent with the pattern of actual values.

The ARIMA model fitting was adequate for the data. ARIMA methodology does have its limitations: The parameters for the tentative model from the identification step were estimated using the ARIMA module in R Software.

Results

The study estimates show the highest point forecast for the rate of confirmed COVID-19 cases in Zambia across the LMICs. The confirmed cases in Zambia were forecast to be at 1,339 on June 9.

As a result, the confirmed rate of disease for Zambia as a whole will increase approximately at the rate of 2.13 per cent for daily confirmed cases. The base data was taken from December 31, 2019 to May 30, 2020 for daily confirmed and death cases.

Based on this data, these confirmed and death cases were predicted between May 31 and June 9. Among these 40 LMICs, the lowest rate of death cases was estimated in Morocco to be 0.48 per cent.

Higher forecasting rate of confirmed cases in Zambia across the LMICs using ARIMA

Date

Confirmed

Deaths

Point Estimate

95% confidence intervals

Point Estimate

95% confidence intervals

May 31, 2020

1099.19

1031.01

1167.37

7.10

6.28

7.91

June 1, 2020

1111.08

1018.83

1203.32

7.19

6.04

8.35

June 2, 2020

1139.69

1012.07

1267.30

7.29

5.88

8.71

June 3, 2020

1168.30

1007.68

1328.92

7.39

5.76

9.02

June 4, 2020

1196.91

1003.97

1389.84

7.49

5.66

9.31

June 5, 2020

1225.52

1000.27

1450.76

7.58

5.58

9.58

June 6, 2020

1254.13

996.23

1512.02

7.68

5.52

9.84

June 7, 2020

1282.74

991.67

1573.80

7.78

5.47

10.09

June 8, 2020

1311.35

986.48

1636.21

7.88

5.43

10.32

June 9, 2020

1339.96

980.59

1699.33

7.97

5.39

10.55

Lower forecasting rate of confirmed and death cases in Morocco across LMICs using ARIMA

Date

Confirmed

Deaths

Point Estimate

95% confidence intervals

Point Estimate

95% confidence intervals

May 31, 2020

7770.03

7697.56

7842.49

202.51

197.98

207.03

June 1, 2020

7826.05

7701.53

7950.58

203.01

195.71

210.31

June 2, 2020

7882.08

7702.01

8062.15

203.52

193.44

213.59

June 3, 2020

7938.11

7697.97

8178.24

204.02

191.06

216.99

June 4, 2020

7994.13

7689.36

8298.91

204.53

188.54

220.52

June 5, 2020

8050.16

7676.33

8423.99

205.04

185.86

224.21

June 6, 2020

8106.19

7659.07

8553.30

205.54

183.04

228.05

June 7, 2020

8162.21

7637.77

8686.65

206.05

180.06

232.04

June 8, 2020

8218.24

7612.62

8823.86

206.55

176.93

236.18

June 9, 2020

8274.27

7583.76

8964.77

207.06

173.67

240.45

Highest number of confirmed and death cases in India across LMICs using ARIMA

Date

Total cases

Total deaths

Point Estimate

95% confidence intervals

Point Estimate

95% confidence intervals

May 31, 2020

182085.73

181625.1

182546.4

5189.78

5159.35

5220.21

June 1, 2020

190570.77

189637.6

191504

5408.55

5354.08

5463.03

June 2, 2020

198808.63

197155

200462.3

5627.33

5546.34

5708.32

June 3, 2020

207029.56

204495.9

209563.3

5846.11

5735.94

5956.27

June 4, 2020

215392.71

211929.2

218856.2

6064.88

5922.99

6206.78

June 5, 2020

223723.16

219246.7

228199.6

6283.66

6107.62

6459.69

June 6, 2020

231984.34

226385.1

237583.6

6502.43

6289.99

6714.88

June 7, 2020

240284.18

233485

247083.3

6721.21

6470.20

6972.22

June 8, 2020

248611.03

240549.5

256672.6

6939.99

6648.38

7231.60

June 9, 2020

256908.42

247510.6

266306.2

7158.76

6824.60

7492.93

Source for all above tables: Authors

The above figures show the highest and lowest rate of confirmed and death cases for COVID-19. Based on the size of the population, India has the highest number of confirmed cases compared to other LMICs.

The total number of confirmed cases in India by June 9, 2020 was said to be 256,908, followed by Pakistan, Ukraine, Bangladesh, Indonesia, Philippines and the remaining LMICs as of June 9.

Similarly, it was estimated that India had the greatest number of deaths forecast at 7,158, followed by Indonesia, Philippines, Pakistan, Egypt, Ukraine, Bangladesh, Moldova, and other remaining LMICs respectively by June 9.

Conclusion

Having the highest share of the population, struggling for daily sustenance and living in poor socio-economic environments, the spread of the disease is easier in LMICs than in developed countries.

Though data was not available on death cases in nine of 40 LMICs, predictions were not possible for them in terms of deaths.

If we go by the highest rate of point forecasts for confirmed cases, Zambia would outnumber other LMICs till June 9, according to the forecast.

What makes this pandemic particularly difficult for countries like Zambia are structural constraints that already exist in their economies, notable socio-economic inequalities and the highest labour force in the informal sector: The country may face severe challenges in handling the pandemic.

In the highest rate of point forecast for death cases, Senegal may be at the top of the list of all 40 LMICs. The country already faces concerns over basic hygiene needs, health infrastructure and people living in unfavourable conditions.

If we go by numbers and population size, India will outnumber other countries in terms of both confirmed and death cases, this study predicted.

The country accommodating the second-largest share of the population, having varied socio-economic and demographic conditions, facing tremendous challenges in terms of the COVID-19 outbreak.

Lowest confirmed cases will be recorded in Tunisia and the lowest deaths have been forecasted in Morocco.

It is time for LMICs to get ahead of the curve and respond to the emergency. Inadequate healthcare resources, financing mechanisms and constrained health infrastructure in LMICs resulted in an escalated rate of infections, followed by a higher incidence of mortality.

LMICs must prioritise evidence-based preventive measures in the pre-crisis phase and require preventive measures along with treatment and counselling during and after the crisis.

It needs to be recognised that prevention and overall management of such health crises may be more difficult in LMICs than in developed countries.

Views expressed are the author's own and don't necessarily reflect those of Down To Earth.

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