All diseases | Rift Valley fever | Zika disease | CCHF | Ebola and Marburg disease | Lassa fever | MERS | Nipah and Henipa virus | SARS | |
Number of articles (%) | 58 (100) | 21 (36) | 13 (22) | 8 (14) | 6 (10) | 5 (9) | 4 (7) | 2 (3) | 2 (3) |
Date range | |||||||||
2010–2019 | 47 (81) | 15 | 13 | 6 | 5 | 4 | 4 | 2 | |
2000–2009 | 9 (16) | 4 | 2 | 1 | 1 | 2 | |||
1990–1999 | 2 (3) | 2 | |||||||
Region of study | |||||||||
African continent | 28 (48) | 18 | 1 | 6 | 5 | ||||
Asia-Pacific | 4 (7) | 3 | 1 | ||||||
Europe | 3 (5) | 1 | 1 | 1 | 1 | ||||
Middle East | 9 (16) | 2 | 6 | 1 | |||||
North America | 2 (3) | 1 | 1 | ||||||
Latin America and Caribbean* | 5 (9) | 5 | |||||||
Global | 8 (14) | 1 | 3 | 2 | 2 | ||||
Prediction methodology | |||||||||
Risk mapping | 26 (45) | 14 | 6 | 1 | 3 | 2 | 1 | 1 | |
Regression model | 21 (36) | 7 | 5 | 4 | 3 | 1 | 1 | ||
Time series forecasting | 23 (40) | 9 | 5 | 4 | 2 | 1 | 2 | ||
Qualitative | 7 (12) | 4 | 2 | 2 | 1 | 1 | |||
Other quantitative | 9 (16) | 1 | 2 | 1 | 1 | 2 | 1 | 1 | 2 |
Species niche model | 15 (26) | 3 | 4 | 1 | 5 | 3 | 1 | ||
Machine learning | 16 (28) | 3 | 6 | 2 | 2 | 2 | 1 | ||
Spatiotemporal model | 25 (43) | 13 | 4 | 4 | 2 | 2 | |||
Internet/phone/computer† | 6 (10) | 5 | 1 | ||||||
Early warning system** | 17 (29) | 7 | 5 | 3 | 1 | 1 | |||
Incidence modelling | 11 (19) | 5 | 5 | 1 | |||||
Model type | |||||||||
Deterministic | 6 (10) | 2 | 1 | 1 | 1 | 1 | |||
Stochastic | 35 (60) | 11 | 9 | 6 | 3 | 3 | 2 | 1 | |
Mixed | 6 (10) | 1 | 1 | 1 | 2 | 1 | 2 | ||
Not applicable/not stated | 11 (19) | 7 | 2 | 1 | 1 | 1 | |||
Data sources | |||||||||
Case data | 47 (81) | 12 | 12 | 7 | 6 | 5 | 3 | 2 | 2 |
Other patient health data | 13 (22) | ||||||||
Meteorological/climate | 39 (67) | 19 | 7 | 5 | 4 | 3 | 1 | ||
Vector/host | 31 (53) | 13 | 7 | 4 | 5 | 3 | 1 | ||
Sociodemographic | 24 (41) | 7 | 7 | 3 | 3 | 2 | 2 | 1 | 1 |
Behaviour (way of infection) | 8 (14) | 2 | 1 | 1 | 1 | 2 | 1 | ||
Healthcare | 5 (9) | 1 | 2 | 1 | 1 | 1 | 1 | ||
Transportation | 12 (21) | 2 | 4 | 1 | 2 | 2 | 1 | 2 | |
Internet† | 7 (12) | 1 | 5 | 1 | |||||
Geographical | 32 (55) | 15 | 13 | 6 | 5 | 4 | 4 | 2 | |
Economic | 9 (16) | 2 | 4 | 1 | 1 | 2 | 1 | ||
Ecological | 18 (31) | 9 | 3 | 2 | 4 | 2 | |||
Expert opinion | 5 (9) | 4 | 1 | 1 | |||||
Other‡ | 6 (10) | 1 | 1 | 1 | 3 | 2 | |||
Prediction outcome | |||||||||
Future cases | 21 (36) | 1 | 9 | 5 | 1 | 3 | 2 | ||
Outbreak risk factors | 37 (64) | 19 | 4 | 7 | 6 | 3 | 1 | ||
Immunity parameters§ | 7 (12) | 2 | 1 | 1 | 1 | 2 | |||
Risk maps | 29 (50) | 4 | 2 | 1 | 1 | 2 | |||
Spatial prediction | 44 (76) | 19 | 10 | 5 | 4 | 4 | 1 | 1 | 2 |
Temporal prediction | 39 (67) | 15 | 7 | 6 | 4 | 2 | 3 | 1 | 1 |
Outbreak risk | 36 (62) | 14 | 9 | 3 | 5 | 3 | 2 | 2 | 1 |
Spillover events | 6 (10) | 2 | 2 | 3 | 2 | ||||
Bio-Env-Econ consequences¶ | 4 (7) | 3 | 1 | 1 | |||||
Env transmission suitability | 20 (34) | 10 | 4 | 2 | 4 | 2 | 1 | ||
Population at risk | 8 (14) | 3 | 2 | 2 | 3 | ||||
Introduction risk | 5 (9) | 1 | 2 | 2 | 1 | 1 | 1 | ||
Effect of climate change | 3 (5) | 2 | |||||||
Epidemic dynamics | 17 (29) | 3 | 4 | 4 | 2 | 1 | 2 | 1 | |
Implementation of prediction/methods by decision makers | |||||||||
Yes | 6 (10) | 4 | 1 | 1 | |||||
Suggested | 30 (52) | 10 | 4 | 6 | 4 | 4 | 3 | 1 | 1 |
No | 22 (38) | 7 | 8 | 2 | 1 | 1 | 1 | 1 | 1 |
Predictions validated against future outbreak data | |||||||||
Yes | 24 (41) | 10 | 3 | 4 | 3 | 1 | 1 | 2 | |
No | 34 (59) | 11 | 10 | 4 | 3 | 4 | 4 | 1 |
For detailed definitions, see online supplementary material.
*Includes South and Middle America.
†Internet and phone-based system/app/computer programme.
‡Non-categorisable data types.
§Reproduction number (R value).
¶Biological, environmental or economic consequences.
**Or proposed Early Warning System.
CCHF, Crimean-Congo haemorrhagic fever; MERS, Middle East respiratory syndrome; SARS, severe acute respiratory syndrome.