Evidence map›Paper›PMID 36568658›Full record

ArticleHeliyon2022

A mathematical prediction model of infectious diseases considering vaccine and temperature, and its prediction in Hong Kong.

Hao Ai, Qiubao Wang, Wei Liu

Abstract read
In one paragraph

Article in Heliyon, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Hao AiDepartment of Mathematical and Physics, Shijiazhuang Tiedao University, 050043, China.
Qiubao WangDepartment of Mathematical and Physics, Shijiazhuang Tiedao University, 050043, China.
Wei LiuDepartment of Mathematical and Physics, Shijiazhuang Tiedao University, 050043, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Since 2020, COVID-19 has launched a rather difficult challenge in public health all over the world. For the current situation of COVID-19 outbreaks in various places, predicting the trend of COVID-19 plays a vital role in later policy-making, allocating medical materials, and developing the economy and society. Although there may be corresponding specific drugs soon, which can reduce the mortality of COVID-19 to a relatively low level, but the main popular Omicron BA.1 and BA.2 strain is highly infectious, which has a great impact on the elderly population, so COVID-19 cannot be equated with general influenza. This paper aims to predict the trend of the number of infections over time and the final number of infections from the fifth wave of epidemic in Hong Kong. To establish a more practical infection model, this paper introduces an infectious disease transmission model with the influence of temperature and vaccine. The model shows that the fifth wave of the epidemic in Hong Kong will end at the end of April 2022. At that time, the cumulative number of infections is expected to reach about 1.6 million. By formulating and implementing reasonable policies, the final number of infections can be controlled at about 1.1 million. Therefore, we hope that the policymakers and managers of COVID-19 in Hong Kong will formulate and adopt reasonable measures to control these epidemic.

Indexed as

COVID-19Hong KongSIR modelTemperature and vaccine

Identifiers

PMID36568658
PMCPMC9767883

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.