Evidence map›Paper›PMID 27564783›Full record

ArticleHealth security

Malware and Disease: Lessons from Cyber Intelligence for Public Health Surveillance.

Frank L Smith

Abstract read
In one paragraph

Article in Health security. 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

1 author.

Frank L SmithFrank L. Smith III, PhD, is a Senior Lecturer, Centre for International Security Studies, University of Sydney , New South Wales, Australia .

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Malicious software and infectious diseases are similar is several respects, as are the functional requirements for surveillance and intelligence to defend against these threats. Given these similarities, this article compares and contrasts the actors, relationships, and norms at work in cyber intelligence and disease surveillance. Historical analysis reveals that civilian cyber defense is more decentralized, private, and voluntary than public health in the United States. Most of these differences are due to political choices rather than technical necessities. In particular, political resistance to government institutions has shaped cyber intelligence over the past 30 years, which is a troubling sign for attempts to improve disease surveillance through local, state, and federal health departments. Information sharing about malware is also limited, despite information technology being integral to cyberspace. Such limits suggest that automation through electronic health records will not automatically improve public health surveillance. Still, certain aspects of information sharing and analysis for cyber defense are worth emulating or, at the very least, learning from to help detect and manage health threats.

Indexed as

Computer SecurityElectronic Health RecordsHumansInformation DisseminationPoliticsPublic Health SurveillancePublic PolicySecurity MeasuresSoftwareUnited StatesUnited States Public Health Service

Identifiers

PMID27564783
PMCPMC5041502

What OpenQuestion holds

Textmetadata
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.