# SARS-CoV-2 and Rohingya Refugee Camp, Bangladesh: Uncertainty and How the Government Took Over the Situation

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## Abstract

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## Simple Summary

## Abstract

**Background:**Bangladesh hosts more than 800,000 Rohingya refugees from Myanmar. The low health immunity, lifestyle, access to good healthcare services, and social-security cause this population to be at risk of far more direct effects of COVID-19 than the host population. Therefore, evidence-based forecasting of the COVID-19 burden is vital in this regard. In this study, we aimed to forecast the COVID-19 obligation among the Rohingya refugees of Bangladesh to keep up with the disease outbreak’s pace, health needs, and disaster preparedness.

**Methodology and Findings:**To estimate the possible consequences of COVID-19 in the Rohingya camps of Bangladesh, we used a modified Susceptible-Exposed-Infectious-Recovered (SEIR) transmission model. All of the values of different parameters used in this model were from the Bangladesh Government’s database and the relevant emerging literature. We addressed two different scenarios, i.e., the best-fitting model and the good-fitting model with unique consequences of COVID-19. Our best fitting model suggests that there will be reasonable control over the transmission of the COVID-19 disease. At the end of December 2020, there will be only 169 confirmed COVID-19 cases in the Rohingya refugee camps. The average basic reproduction number (${\mathcal{R}}_{0}$) has been estimated to be 0.7563.

**Conclusions:**Our analysis suggests that, due to the extensive precautions from the Bangladesh government and other humanitarian organizations, the coronavirus disease will be under control if the maintenance continues like this. However, detailed and pragmatic preparedness should be adopted for the worst scenario.

## 1. Introduction

## 2. Methodology

#### 2.1. Mathematical Model and Formulation

#### 2.2. Equilibrium Points

#### 2.3. Disease-Free Equilibrium Point

#### 2.4. Basic Reproduction Number

#### 2.5. Positivity and Boundedness of Solutions

**Theorem**

**1.**

**Proof.**

## 3. Data

## 4. Results

#### 4.1. Numerical Illustrations, Data Fitting, and Model Validation

- First, we have tried our best to counterfeit the real data with the model generated forecast scenario.
- Then, we related the model result to be uninfluenced partially from the very fluctuating real data. These results warn about the worst scenario of this pandemic in this camp if initial strict initiatives were failed to be implemented or if the situation gets out of control for any other reason.

#### 4.2. Best Fitting Data

#### 4.3. Good Fitting Data

#### 4.4. Discussion

## 5. Conclusions

## Author Contributions

## Funding

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## Abbreviations

COVID-19 | Coronavirus diseases |

SEIR | Susceptible-asymptomatically infected-infectious-recovered |

SARS-CoV-2 | Severe acute respiratory syndrome coronavirus 2 |

FDMN | Forcibly displaced Myanmar nationals |

UNHCR | United Nations High Commissioner for Refugees |

SARI | Severe acute respiratory illness |

ITC | Isolation and treatment center |

GAM | Global acute malnutrition |

SIR | Susceptible-infectious-recovered |

DFE | Disease-free equilibrium |

## Appendix A

**Figure A2.**Visual presentation of Gender disparity of a model that has a good fitting with real data.

Notation | Interpretations | Notation | Interpretations |
---|---|---|---|

$\beta $ | Transition rate from E to I class | ${\mu}_{1}$ | Natural death rate |

$\Lambda $ | Recruitment rate in S class | ${\mu}_{2}$ | Disease induced death rate |

$\sigma $ | Transmission rate from S to E & I classes | ${\gamma}_{1}$ | Recovery rate of E class |

${S}_{0}$ | Initial population in S | ${\gamma}_{2}$ | Recovery rate of I class |

${E}_{0}$ | Initial population in E | ${I}_{0}$ | Initial population in I |

Parameters | Description | Value (Best Fit) | Value (Good Fit) | References |
---|---|---|---|---|

${S}_{0}$ | Susceptible population | $860,243$ | $860,243$ | [3] |

on 13 March 2020 (aprox.) | ||||

${E}_{0}$ | asymptomatically infected | 100 | 30 | Assumed |

population on 13 March 2020 (aprox.) | ||||

${I}_{0}$ | Infectious population on 13 March 2020 | 1 | 1 | [26] |

${R}_{0}$ | Recovered population on 13 March 2020 | 0 | 0 | [26] |

$\Lambda $ | Per day average birth | 60 | 60 | [34] |

$\beta $ | Per day transition rate from E to I | $0.0033$ | $0.0140$ | Assumed |

$\sigma $ | Per day transmission rate | 1.1623 $\times \phantom{\rule{3.33333pt}{0ex}}{10}^{-6}$ | 1.1624 $\times \phantom{\rule{3.33333pt}{0ex}}{10}^{-6}$ | Estimated |

from S to E & I | ||||

${\gamma}_{1}$ | Per day recovery rate of E | $0.9966$ | $0.9900$ | Assumed |

${\gamma}_{2}$ | Per day recovery rate of I | $0.3205$ | $0.3205$ | Estimated |

${\mu}_{1}$ | Per day natural death rate | 9.2997 $\times \phantom{\rule{3.33333pt}{0ex}}{10}^{-5}$ | 9.3019 $\times \phantom{\rule{3.33333pt}{0ex}}{10}^{-5}$ | [35] |

${\mu}_{2}$ | Per day disease induced death rate | $0.0694$ | $0.0694$ | Estimated |

${\mathcal{R}}_{0}$ | Average basic reproduction number | $0.7563$ | $0.7737$ | Estimated |

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**Figure 1.**Flow diagram of the modified SEIR model (3).

**Figure 3.**Visual presentation of gender disparity (Male cases versus Female cases) for model best fitting with real data.

**Figure 5.**Infection percentage comparison between Males and Females for real data and both best and good fit.

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**MDPI and ACS Style**

Kamrujjaman, M.; Mahmud, M.S.; Ahmed, S.; Qayum, M.O.; Alam, M.M.; Hassan, M.N.; Islam, M.R.; Nipa, K.F.; Bulut, U. SARS-CoV-2 and Rohingya Refugee Camp, Bangladesh: Uncertainty and How the Government Took Over the Situation. *Biology* **2021**, *10*, 124.
https://doi.org/10.3390/biology10020124

**AMA Style**

Kamrujjaman M, Mahmud MS, Ahmed S, Qayum MO, Alam MM, Hassan MN, Islam MR, Nipa KF, Bulut U. SARS-CoV-2 and Rohingya Refugee Camp, Bangladesh: Uncertainty and How the Government Took Over the Situation. *Biology*. 2021; 10(2):124.
https://doi.org/10.3390/biology10020124

**Chicago/Turabian Style**

Kamrujjaman, Md., Md. Shahriar Mahmud, Shakil Ahmed, Md. Omar Qayum, Mohammad Morshad Alam, Md Nazmul Hassan, Md Rafiul Islam, Kaniz Fatema Nipa, and Ummugul Bulut. 2021. "SARS-CoV-2 and Rohingya Refugee Camp, Bangladesh: Uncertainty and How the Government Took Over the Situation" *Biology* 10, no. 2: 124.
https://doi.org/10.3390/biology10020124