Prediction and Real-Time Tracking of a Pandemic with BIORADAR

REPORT OF REPLIKIN COUNTS IN THE H1N1 VIRUS IN HUMANS BEFORE AND DURING THE 2009 PANDEMIC. (X= REPLIKIN COUNT, – # REPLIKINS PER 100 AMINO ACIDS, Y=DATE), INFECTIVITY GENE (RED), LETHALITY GENE (BLACK) ​

3D X-Ray diffraction visualization of Replikin sequences increasing on the surface of the H1N1 HA gene prior to the 2009 H1N1 Pandemic

BIORADAR SURVEILLANCE
EBOLA, H5N1, H1N1 HAND FOOT AND MOUTH DISEASE

The inability to predict outbreaks has had severe global impact. Transmissibility  has been accelerated by globalization through cross border and continental trade and travel. Lethal outbreaks have occurred for centuries without advance warning.  In the past, there has been insufficient time to prepare counter-measures and specific response before a virus strikes or escalates into a pandemic. The Replikins Companies have made substantial breakthroughs in prediction and disease surveillance based on quantitative computer driven analysis of replication in the genome that make such preparedness and rapid response. Replikins’ genomic software detects changes in the disease genome up to two years in advance outbreaks, which signal what will be the severity of the outbreak, its geographical location and the species that will be most affected. 

The BIORADAR surveillance softwares predicts not only the strain of outbreaks but the geographic location and organisms in which the pathogen will strike. The Replikins surveillance software has the potential to make a significant contribution to global health and pandemic preparedness. Replikins diseases surveillance software measures the infectivity and lethality of a strain and tracks and predicts the geographic location of outbreaks as well as the genomic structure of the disease which will strike.  The software then tracks, real time, the progression of the outbreak. Replikins sequences can be both seen [LINK] and counted [LINK].

REPLIKINS BIORADAR SOFTWARE PREDICTS 2009 H1N1 PANDEMIC MORE THAN A YEAR IN ADVANCE

Replikins publishes warning on April 7, 2008 of H1N1 Pandemic threat:

“H1N1 Influenza Virus With Highest Replikin Count™ Since the 1918 Pandemic Identified in the U.S. and Austria (April 7, 2008) Replikins, Ltd. has found that the Replikin Count™ of the H1N1 strain of influenza virus has recently increased to 7.6 (plus/minus 1.4), its highest level since the 1918 H1N1 pandemic (p value less than 0.001). A rising Replikin Count of a particular influenza strain, indicating rapid replication of the virus, is an early warning which has been followed consistently by an outbreak of the specific strain…” http://www.prweb.com/releases/2008/04/prweb835974.htm

“On June 11, 2009, the World Health Organization signaled that a global pandemic of novel influenza A (H1N1) was underway by raising the worldwide pandemic alert level to PHASE 6.” http://www.cdc.gov/h1n1flu/who/ http://www.who.int/mediacentre/news/statements/2009/h1n1_pandemic_phase6_20090611/en/

REPLIKIN COUNT INCREASE PREDICTS 2014 EBOLA OUTBREAK

BIORADAR vs. CDC EBOLA PREDICTIONS

September 26, 2014: The CDC Predicts 1,400,000 cases of Ebola could occur by January 2015:

“The ebola epidemic could claim hundreds of thousands of lives and infect more than 1,400,000 people by the end of January, according to a statistical forecast released this week by the U.S. Centers for Disease Control and Prevention. [CDC]” Science Daily, September 26, 2014

October 9, 2014 Replikins publishes that sharp drop in Replikin Count in Ebola gene signals early end to the Ebola outbreak.

INCREASE IN REPLIKIN COUNT

before Hand-Foot-and-Mouth disease outbreaks in China

Replikin Counts pB1 gene plotted against cases of H5N1 (Global)

Zika Gene Molecular Evolution and Mutations 1947-2016

Individual Replikin Counts preceeding outbreaks in Africa 1947-2012, French Polynesia 2013, Easter Island 2014, Brazil 2015, Global 2016 N=116

H1N1 vs. EBOLA

DIFFERENT PROGRESSIONS OF OUTBREAKS PRECEEDED BY CHANGES IN REPLIKIN COUNTS

GEOGRAPHIC LOCALIZATION

Localization of Replikin Peak Gene of Human H5N1 Virus in Indonesia and six other countries

HOST LOCALIZATION

Double differentiation

Replikin Counts of infectivity and lethality Genes in H1N1 and H5N1

REPLIKIN COUNT INCREASE PREDICTS MEXICO- U.S. OUTBREAK AND H1N1 PANDEMIC (2001-2009)

‘`April 2008, Replikin warns: Replikin count “Increase to 7.6 (plus/minus 1.4) highest level since the 1918 H1N1 pandemic…”1
June 11, 2009 WHO declares H1N1 Pandemic2

REPLIKIN COUNT INCREASE PREDICTS H3N2 OUTBREAK IN ONTARIO CANADA

BIORADARR

  • Over 3 million gene sequences analyzed to date for over 41 outbreaks in 16 viruses
  • provides for the first time, one to two years advance notice of outbreaks.
  • quantitative analysis of “big” sequence data worldwide (PubMed and other databases).
  • real time and retrospective analysis
  • in as little as 2 to 12 hours
  • identifies which virus will strike
  • which host (human and/or animal) tracks zoonosis
  • where it will strike (geographic localization)
  • how lethal and how infective the pathogen is
  • monitors independent portions of the gene related to infectivity and lethality.

Surveillance Methods

  • software algorithm for counting the replikin peptides in each genomic sequence (Replikin Count = number of replikins per 100 amino acids).
  • increases and decreases in Repkikin count are plotted for strain, host, country, history, morbidity, and lethality.
  • for each group of specimens, the mean and standard deviation of the mean (SD) are calculated and compared over time periods . Higher standard deviation or spread in Replikin count in sample base signals pending outbreak
  • during outbreaks, Replikin Counts were compared to Counts for the same strain in non-outbreak time periods.
  • terms ‘increase’ and ‘decrease’ of Replikin Counts are used only when the p levels are less than 0.001.
    Statistical analyses of rate of change, trend, pattern, and growth models in the evolution of each virus strain are conducted. Independent statistical analysis by Professor Todd MacKenzie, of Dartmouth Medical School of the changes in the distribution of Replikin count in H1N1 virus specimens by year over the calendar period 2001-2010; for example for years 2006 to 2008 the count increased to a mean of 1.0 (95% Conf. Int. 0.8 to 1.1) per year which was statistically significant: p-value (linear regression) = 1/1029, p-value (Spearman correlation) < 2/10.. Nature Precedings doi:10.1038/npre.2011.6279.1