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amicrobialworld | – How much do you know? — 18 websites ranked by shared content topics, category and on-page relevance.

Each result shows its full tech stack, contacts and AI-policy — not just a name · Browse all sites in Infectious Diseases →

Domain	Match	Title	Country/Lang	Category	AI files	Contact	AI-protection
agargroup.org.au ↗	64 match 1 shared topics	AGAR Group – The Australian Group on Antimicrobial Resistance	Australia en	infectious-diseases WordPress	robotsllmsaihumans	emailphone	none
asainc.net.au ↗	63 match 1 shared topics	Australian Society for Antimicrobials – Australian Society for Antimicrobials	Australia en	infectious-diseases WordPress WooCommerce	robotsllmsaihumans	emailphone	partial · 8
amymaypointer.com ↗	63 match 1 shared topics	Amy-May Pointer \| Microbiologist	en	infectious-diseases	robotsllmsaihumans	emailphone	none
leekshaffer.com ↗	63 match 1 shared topics	Lee Kennedy-Shaffer, PhD – Assistant Professor, Biostatistics, Yale School of Public Health	en	infectious-diseases WordPress	robotsllmsaihumans	emailphone	none
leegionella.com ↗	63 match 1 shared topics	Leegionella Ltd - Independent Public Health Microbiology consultancy and Advisory Services	en	infectious-diseases WordPress WooCommerce	robotsllmsaihumans	emailphone	none
coronavirus.gov ↗	63 match 1 shared topics	Lab Leak: The True Origins of Covid-19 – The White House	en	infectious-diseases WordPress	robotsllmsaihumans	emailphone	none
covid.gov ↗	63 match 1 shared topics	Lab Leak: The True Origins of Covid-19 – The White House	en	infectious-diseases WordPress	robotsllmsaihumans	emailphone	none
covidtests.gov ↗	63 match 1 shared topics	Lab Leak: The True Origins of Covid-19 – The White House	en	infectious-diseases WordPress	robotsllmsaihumans	emailphone	none
5gfree.org ↗	63 match 1 shared topics	5gfree.org – Activating and Educating the World About 5G	en	infectious-diseases WordPress	robotsllmsaihumans	emailphone	none
zplussurakhyaseva.com ↗	63 match 1 shared topics	Z Plus Surakhya Seva – Taking Command Of Your Security Needs	en	infectious-diseases WordPress WooCommerce	robotsllmsaihumans	emailphone	none
professorkoakinyemi.com ↗	62 match 1 shared topics	Prof. Kabiru Olusegun Akinyemi \| Professor of Microbiology, LASU	en	infectious-diseases	robotsllmsaihumans	emailphone	none
ke-lab.org ↗	62 match 1 shared topics	Ke lab – Multiscale viral and immune dynamics @ Los Alamos National Laboratory	en	infectious-diseases WordPress	robotsllmsaihumans	emailphone	none
kanjeelab.org ↗	62 match 1 shared topics	Kanjee Lab – Washington University in St Louis School of Medicine	en	infectious-diseases WordPress	robotsllmsaihumans	emailphone	none
1goodthingtoday.org ↗	62 match 1 shared topics	1 Good Thing Today \| How to Help Your Community During COVID-19	en	infectious-diseases WordPress	robotsllmsaihumans	emailphone	none
ccid.org ↗	62 match 1 shared topics	Center for Complex Infectious Diseases	en	infectious-diseases	robotsllmsaihumans	emailphone	none
prepareforcovid.com ↗	62 match 1 shared topics	Prepare for Covid-19 – Keeping you up to date on the pandemic	en	infectious-diseases WordPress	robotsllmsaihumans	emailphone	none
ccpatwar.org ↗	62 match 1 shared topics	The CCP is at War with America – The Chinese Communist Party’s COVID-19 Biological Warfare Attack and What’s Next	en	infectious-diseases WordPress	robotsllmsaihumans	emailphone	none
cslseqirus.ca ↗	62 match 1 shared topics	CSL Seqirus \| A World Leader in Influenza Vaccines	Canada en	infectious-diseases	robotsllmsaihumans	emailphone	none

How the match score works

Each match is a 0–100 similarity score — the higher it is, the more two sites resemble one another. It’s computed automatically from our own crawl data (never from what a site says about itself) by combining several independent signals, so a high score means several of them point the same way:

shared topics how many of the same fine‑grained content topics (IAB sub‑categories) both sites cover — the main signal.
same category whether they sit in the same business / industry category.
content & title how closely their titles and page descriptions line up in wording and meaning.

No single signal decides the result — they’re blended together. Treat the score as a way to rank candidates rather than an absolute percentage; the chips on each result show which signals contributed.