Introduction
Literature review
No | Author(s) | Year | Title | Journal | Perspective | Results |
---|---|---|---|---|---|---|
1 | Radcliffe [107] | 1862 | On the recent epidemic of diphtheria | The Lancet | Medical Science | Not specified |
2 | (unknown) | 1865 | The cholera | The Lancet | Medical Science | Not specified |
3 | Sykes | 1890 | The influenza epidemic of 1889–1890 | Public Health | Public Health | Fast dispersion of pandemic from Asia to Europe within weeks; comparison of influenza and dengue as north–south split |
4 | Kartman [75] | 1957 | The concept of vector efficiency in experimental studies of plague | Experimental Parasitology | Public Health | Mathematical models for epidemic modeling |
5 | Bloom and Mahal [18] | 1997 | Does the AIDS epidemic threaten economic growth? | Journal of Econometrics | Economics | Connection of epidemic events to economic progressions |
6 | Blount et al. [19] | 1997 | Nonlinear and dynamic programming for epidemic intervention | Applied Mathematics and Computation | Mathematics | Epidemics as case study examples for mathematical modeling |
7 | Mesnard and Seabright [91] | 2009 | Escaping epidemics through migration? Quarantine measures under incomplete information about infection risk | J. of Public Economics | Public Economics | Effectiveness of quarantine measures given incomplete personal information and the motivation/decision to embark on migration to evade individual infections |
8 | Dasaklis et al. [34] | 2012 | Epidemics control and logistics operations: A review | Int. J. of Production Economics | Management Science | Impact of epidemics on production and logistics environments and management |
9 | Naevdal [95] | 2012 | Fighting transient epidemics – optimal vaccination schedules before and after an outbreak | Health Economics | Public Economics | Showing increasing returns to scale for a vaccination with influenza as an example |
10 | Cao et al. [24] | 2017 | Global stability of an age-structure epidemic model with imperfect vaccination and relapse | Physica A: Statistical Mech.& its Applications | Mathematics | Modeling of interventions for global epidemic events |
11 | Kostova et al. [83] | 2019 | Long‐distance effects of epidemics: Assessing the link between the 2014 West Africa Ebola outbreak and U.S. exports and employment | Health Economics | Public Economics | Economic transfer effects of epidemic and pandemic events with the example of Africa and the USA from 2014 |
12 | Zhai et al. [136] | 2020 | The epidemiology, diagnosis, and treatment of COVID-19 | Int. J. of Antimicrobial Agents | Medical Science | Specific results regarding COVID-19 treatment from a medical perspective |
13 | Singh et al. [116] | 2020 | Internet of things (IoT) applications to fight against COVID-19 pandemic | Diabetes & Metabolic Syndrome | Medical Science, Computer Science | Expectations and results regarding IoT concepts and instruments anti-pandemic |
14 | Bontempi et al. [21] | 2020 | Understanding COVID-19 diffusion requires an interdisciplinary, multi-dimensional approach | Environmental Research | Environmental Science | The requirement of an interdisciplinary approach toward COVID-19 measures |
15 | da Silva et al. [33] | 2020 | Forecasting Brazilian and American COVID-19 cases based on artificial intelligence coupled with exogenous climatic variables | Chaos, Solitons & Fractals | Mathematics | Pandemic case prognosis with specific models, including temperature and weather data |
16 | Eberhardt et al. [42] | 2020 | Multi-stage group testing improves efficiency of large-scale COVID-19 screening | J. of Clinical Virology | Medical Science | Testing strategies regarding public policy and decision-making information in a pandemic |
17 | Govindan et al. [60] | 2020 | A decision support system for demand management in healthcare supply chains considering the epidemic outbreaks | Transportation Research Part E | Management Science | Stabilizing global supply chains in pandemic situations |
18 | Wang et al. [130] | 2020 | Psychological impact of Coronavirus Disease 2019 (COVID-19) epidemic on medical staff in different posts in China | J. of Psychiatric Research | Medical Science | Cross-disciplinary effects of pandemics on healthcare staff with the example of China |
19 | Yin et al. [134] | 2020 | Preventing COVID-19 from the perspective of industrial information integration | J. of Industrial Information Integration | Computer Science | Specific information and integration perspective on pandemic countermeasures |
20 | Wang et al. [129] | 2020 | Can masks be reused after hot water decontamination during the COVID-19 pandemic? | Engineering | Engineering Science | Operational question of protective gear reuse in a pandemic situation and with specific hygiene measures |
21 | Kierzkowski and Kisiel [78] | 2020 | Simulation model of security control lane operation in the state of the COVID-19 epidemic | J. of Air Transport Management | Management Science | Extension of pandemic situation management toward airport security management |
22 | Alberti and Faranda [1] | 2020 | On the uncertainty of real-time predictions of epidemic growth: A COVID-19 case study for China and Italy | Communications in Nonlinear Science and Numerical Simulation | Mathematics | Ex-post evaluation of simulation and prognosis approaches in a pandemic |
23 | Yezli and Khan [133] | 2020 | COVID-19 social distancing in the Kingdom of Saudi Arabia | Travel Medicine & Infectious Disease | Public Health | Measures evaluation with Saudi Arabia as the example |
24 | Kawashima et al. [76] | 2020 | The relationship between fever rate and telework implementation as a social distancing measure against the COVID-19 pandemic in Japan | Public Health | Public Health & Public Economics | Interrelation of COVID-19 outbreak and telework measures with Japan as the example |
25 | Saez et al. [113] | 2020 | Effectiveness of the measures to flatten the epidemic curve of COVID-19. The case of Spain | Science of The Total Environment | Public Economics | Effectiveness evaluation of COVID-19 measures with Spain as the example |
26 | Vicentini et al. [127] | 2020 | Early assessment of the impact of mitigation measures on the COVID-19 outbreak in Italy | Public Health | Public Health | Impact of mitigation and lockdown measures from with Italy as the example |
27 | WHO Working Group | 2020 | A minimal common outcome measure set for COVID-19 clinical research | The Lancet | Medical Science | Tackling the data collection and standardization challenge in the COVID-19 outbreak, a common dataset is proposed with three core elements |
28 | Eng Koon [43] | 2020 | The impact of sociocultural influences on the COVID-19 measures—Reflections from Singapore | J. of Pain and Symptom Management | Public Health | Health care systems react to external shocks and challenges differently based on their different socio-cultural backgrounds and values |
29 | Bruinen de Bruin et al. [23] | 2020 | Initial impacts of global risk mitigation measures taken during the combatting of the COVID-19 pandemic | Safety Science | Engineering | Empirical impacts of social distancing and lockdown measures on different public accident and injury areas |
30 | Dawoud [35] | 2020 | Emerging from the other end: Key measures for a successful COVID-19 lockdown exit strategy and the potential contribution of pharmacists | Research in Social and Administrative Pharmacy | Public Economics | Role of pharmacies, interrelation of political measures with medical results |
31 | Chilton et al. [30] | 2020 | Beyond COVID-19: How the ‘dismal science’ can prepare us for the future | Health Economics | Public Economics | Public welfare and balancing editorial and commentary about the trade-offs regarding health economics perspectives on COVID-19 |
32 | Castaldo et al. [25] | 2020 | Safety and efficacy of amiodarone in a patient with COVID-19 | J. of the Am. Coll. of Cardiology—Case Reports | Medical Science | Effects and safety of specific drug use in COVID-19 patients as a secondary challenge for medical treatments |
Data and methodology
Dataset
Data envelopment analysis
Design and application of the DEA model
Empirical results
Comparability of health systems in time-delayed COVID-19 outbreaks
DEA window analysis with panel data for time-dependent COVID-19 development
Mean | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
AUS | 0.96 | 1.00 | 0.97 | 0.95 | 0.95 | 1.00 | 0.95 | 1.00 | 1.00 | 0.89 | 0.83 | 1.00 | 1.00 |
AUT | 0.75 | 0.68 | 0.61 | 0.68 | 0.96 | 1.00 | 0.94 | 0.81 | 0.63 | 0.73 | 0.73 | 0.67 | 0.59 |
BEL | 0.79 | 0.51 | 0.53 | 0.59 | 0.80 | 0.90 | 0.92 | 1.00 | 0.98 | 0.87 | 0.73 | 0.80 | 0.84 |
CAN | 0.94 | 1.00 | 0.79 | 0.86 | 1.00 | 1.00 | 1.00 | 0.98 | 0.89 | 0.87 | 0.96 | 1.00 | 0.93 |
CZE | 0.85 | 0.77 | 0.77 | 0.90 | 0.92 | 1.00 | 0.97 | 0.78 | 0.70 | 0.73 | 0.71 | 0.96 | 1.00 |
DEU | 0.84 | 0.65 | 0.66 | 0.59 | 0.89 | 0.98 | 1.00 | 0.96 | 0.91 | 0.91 | 0.84 | 0.81 | 0.88 |
DNK | 0.96 | 0.94 | 1.00 | 0.90 | 0.91 | 0.92 | 1.00 | 1.00 | 0.97 | 1.00 | 1.00 | 1.00 | 0.89 |
ESP | 0.80 | 0.67 | 0.52 | 0.51 | 0.77 | 0.85 | 1.00 | 1.00 | 0.97 | 0.92 | 0.87 | 0.73 | 0.84 |
FIN | 0.99 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 0.95 | 1.00 | 1.00 | 1.00 | 1.00 | 0.95 | 1.00 |
FRA | 0.69 | 0.58 | 0.63 | 0.77 | 0.76 | 0.78 | 0.69 | 0.58 | 0.54 | 0.60 | 0.77 | 0.86 | 0.75 |
GBR | 0.69 | 0.73 | 0.55 | 0.53 | 0.52 | 0.52 | 0.61 | 0.66 | 0.65 | 0.73 | 0.89 | 0.99 | 0.95 |
IRL | 0.85 | 0.54 | 0.69 | 0.54 | 0.74 | 0.92 | 0.86 | 0.94 | 1.00 | 1.00 | 1.00 | 0.95 | 1.00 |
ITA | 0.74 | 0.77 | 0.56 | 0.52 | 0.51 | 0.52 | 0.63 | 0.80 | 0.82 | 0.85 | 0.94 | 0.98 | 0.98 |
JPN | 0.99 | 1.00 | 1.00 | 0.89 | 1.00 | 0.99 | 0.99 | 1.00 | 1.00 | 1.00 | 1.00 | 0.98 | 1.00 |
KOR | 0.88 | 0.61 | 0.57 | 0.76 | 1.00 | 0.89 | 0.90 | 0.96 | 0.96 | 0.99 | 0.97 | 0.93 | 0.99 |
NLD | 0.99 | 1.00 | 1.00 | 1.00 | 1.00 | 0.96 | 1.00 | 0.94 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 |
NOR | 0.98 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 0.92 | 1.00 | 1.00 | 0.83 | 1.00 | 1.00 |
SVN | 0.68 | 0.56 | 0.98 | 0.86 | 0.69 | 0.70 | 0.72 | 0.65 | 0.60 | 0.67 | 0.57 | 0.55 | 0.66 |
SWE | 0.99 | 1.00 | 1.00 | 1.00 | 0.93 | 1.00 | 0.94 | 0.99 | 0.97 | 1.00 | 0.99 | 1.00 | 1.00 |
Network DEA for pre-epidemic health strategy and COVID-19 testing as an ad hoc intervention
DEA window analysis for the impact of governmental programs on the economy
Mean | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
AUS | 0.98 | 1.00 | 0.99 | 0.99 | 0.99 | 1.00 | 0.97 | 1.00 | 1.00 | 0.92 | 0.87 | 1.00 | 1.00 |
AUT | 0.87 | 1.00 | 0.86 | 0.77 | 0.96 | 1.00 | 0.95 | 0.89 | 0.74 | 0.88 | 0.88 | 0.77 | 0.72 |
BEL | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 |
CAN | 0.95 | 1.00 | 0.85 | 0.90 | 1.00 | 1.00 | 1.00 | 0.98 | 0.89 | 0.87 | 0.96 | 1.00 | 0.93 |
CZE | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 |
DEU | 0.89 | 1.00 | 0.83 | 0.69 | 0.89 | 0.98 | 1.00 | 0.96 | 0.91 | 0.91 | 0.84 | 0.81 | 0.88 |
DNK | 1.00 | 1.00 | 1.00 | 0.99 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 0.96 |
ESP | 0.81 | 0.67 | 0.52 | 0.51 | 0.77 | 0.87 | 1.00 | 1.00 | 0.98 | 0.93 | 0.89 | 0.75 | 0.85 |
FIN | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 0.98 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 |
FRA | 0.81 | 0.92 | 1.00 | 0.98 | 0.95 | 0.93 | 0.77 | 0.60 | 0.57 | 0.61 | 0.80 | 0.88 | 0.77 |
GBR | 0.72 | 0.93 | 0.59 | 0.56 | 0.55 | 0.54 | 0.61 | 0.66 | 0.65 | 0.73 | 0.89 | 0.99 | 0.95 |
IRL | 0.94 | 0.84 | 0.90 | 0.83 | 0.87 | 0.96 | 0.95 | 0.99 | 1.00 | 1.00 | 1.00 | 0.98 | 1.00 |
ITA | 0.76 | 0.78 | 0.57 | 0.53 | 0.52 | 0.52 | 0.64 | 0.83 | 0.87 | 0.89 | 0.96 | 0.98 | 0.98 |
JPN | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 |
KOR | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 0.95 | 1.00 |
NLD | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 |
NOR | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 |
SVN | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 |
SWE | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 |