Posts tagged epidemiology

3 Notes

Highly pathogenic H5N1 transmission risk between domestic poultry and wild Anatidae waterfowl at 1 km resolution for China. Models include H5N1-specific transmission factors and are uni-directional with (A) representing transmission risk from domestic to wild birds, and (B) from wild birds to domestic.
Published in Front. Public Health 1:28. doi: 10.3389/fpubh.2013.00028

Highly pathogenic H5N1 transmission risk between domestic poultry and wild Anatidae waterfowl at 1 km resolution for China. Models include H5N1-specific transmission factors and are uni-directional with (A) representing transmission risk from domestic to wild birds, and (B) from wild birds to domestic.

Published in Front. Public Health 1:28. doi: 10.3389/fpubh.2013.00028

13 Notes

The figure above shows confirmed cases of Middle East Respiratory Syndrome Coronavirus (MERS-CoV) (N = 55) reported as of June 7, 2013, to the World Health Organization and history of travel from the Arabian Peninsula or neighboring countries within 14 days of illness onset, during 2012-2013. All reported cases of MERS-CoV were directly or indirectly linked to one of four countries: Saudi Arabia, Qatar, Jordan, and the United Arab Emirates.
Printed in the Morbidity and Mortality Weekly Report, June 14, 2013 / 62(23);480-483

The figure above shows confirmed cases of Middle East Respiratory Syndrome Coronavirus (MERS-CoV) (N = 55) reported as of June 7, 2013, to the World Health Organization and history of travel from the Arabian Peninsula or neighboring countries within 14 days of illness onset, during 2012-2013. All reported cases of MERS-CoV were directly or indirectly linked to one of four countries: Saudi Arabia, Qatar, Jordan, and the United Arab Emirates.

Printed in the Morbidity and Mortality Weekly Report, June 14, 2013 / 62(23);480-483

2 Notes

Spatial clusters (hotspots) of typhoid in DMA during 2005–2009.
Published in Dewan AM, Corner R, Hashizume M, Ongee ET (2013) Typhoid Fever and Its Association with Environmental Factors in the Dhaka Metropolitan Area of Bangladesh: A Spatial and Time-Series Approach. PLoS Negl Trop Dis 7(1): e1998

Spatial clusters (hotspots) of typhoid in DMA during 2005–2009.

Published in Dewan AM, Corner R, Hashizume M, Ongee ET (2013) Typhoid Fever and Its Association with Environmental Factors in the Dhaka Metropolitan Area of Bangladesh: A Spatial and Time-Series Approach. PLoS Negl Trop Dis 7(1): e1998

1 Notes

Spatial regression between typhoid incidence (per 100,000 people) and distance to water bodies. A) Shows spatial distribution of the t-value, B) shows the parameter estimates.
Published in Dewan AM, Corner R, Hashizume M, Ongee ET (2013) Typhoid Fever and Its Association with Environmental Factors in the Dhaka Metropolitan Area of Bangladesh: A Spatial and Time-Series Approach. PLoS Negl Trop Dis 7(1): e1998

Spatial regression between typhoid incidence (per 100,000 people) and distance to water bodies. A) Shows spatial distribution of the t-value, B) shows the parameter estimates.

Published in Dewan AM, Corner R, Hashizume M, Ongee ET (2013) Typhoid Fever and Its Association with Environmental Factors in the Dhaka Metropolitan Area of Bangladesh: A Spatial and Time-Series Approach. PLoS Negl Trop Dis 7(1): e1998

4 Notes

A. High resolution regionalsensitivity of malaria inresponse to fluctuations intemperature. B. High resolution regional sensitivity of malaria in response to fluctuations in precipitation.
Results at full resolution are shown in this figure. The colours in the figure represent the mean sensitivity to temperature or precipitation and the elevation of the polygons indicate the signal-to-noise ratio (SNR) defined by the ratio of the mean to the standard deviation derived from the bootstrap.
Published in Edlund et al. Malaria Journal 2012 11:331

A. High resolution regionalsensitivity of malaria inresponse to fluctuations intemperature. B. High resolution regional sensitivity of malaria in response to fluctuations in precipitation.

Results at full resolution are shown in this figure. The colours in the figure represent the mean sensitivity to temperature or precipitation and the elevation of the polygons indicate the signal-to-noise ratio (SNR) defined by the ratio of the mean to the standard deviation derived from the bootstrap.

Published in Edlund et al. Malaria Journal 2012 11:331

2 Notes

A. Malaria sensitivity to increasing temperature from WHO data. B. Malaria sensitivity to increasing temperature from simulation. C. Malaria sensitivity to increasing precipitation from WHO data. D. Malaria sensitivity to increasing precipitation from simulation.
Red regions indicate an increase in malaria potential in response to increasing temperature or precipitation while blue regions show a decreased potential in response to increasing temperature or precipitation. White represents regions with no reporting and/or no malaria. The height of the county polygons provides a view of the signal-to-noise ratio determined by bootstrapping.
Published in Edlund et al. Malaria Journal 2012 11:331

A. Malaria sensitivity to increasing temperature from WHO data. B. Malaria sensitivity to increasing temperature from simulation. C. Malaria sensitivity to increasing precipitation from WHO data. D. Malaria sensitivity to increasing precipitation from simulation.

Red regions indicate an increase in malaria potential in response to increasing temperature or precipitation while blue regions show a decreased potential in response to increasing temperature or precipitation. White represents regions with no reporting and/or no malaria. The height of the county polygons provides a view of the signal-to-noise ratio determined by bootstrapping.

Published in Edlund et al. Malaria Journal 2012 11:331

3 Notes

Cases of wild poliovirus type 1 (WPV1), wild poliovirus type 3 (WPV3), and circulating vaccine-derived polio virus type 2 (cVDPV2),* by year — Nigeria, January 2011–September 2012†
*Each instance of a symbol represents one case of poliovirus and is drawn at random within district boundaries.
†Data as of October 30, 2012.

Published in the Morbidity and Mortality Weekly Report for November 9, 2012 / 61(44);899-904

Cases of wild poliovirus type 1 (WPV1), wild poliovirus type 3 (WPV3), and circulating vaccine-derived polio virus type 2 (cVDPV2),* by year — Nigeria, January 2011–September 2012†

*Each instance of a symbol represents one case of poliovirus and is drawn at random within district boundaries.

†Data as of October 30, 2012.


Published in the Morbidity and Mortality Weekly Report for November 9, 2012 / 61(44);899-904

1 Notes

Regional variation of estimated rabies deaths and death rates: India, 2005. State wise death rates are standardized to 2005 UN population estimates for India. Total estimated rabies deaths for India in the present study is 12,700, 99% CI (10,000, 15,500). Areas where no rabies deaths captured by this study represent 7% of the total India population. Abbreviations: Larger states U: AP-Andhra Pradesh, AS-Assam, BR-Bihar, CG-Chhattisgarh, DL-Delhi, GJ-Gujarat, HR-Haryana, JK-Jammu & Kashmir, JH-Jharkhand, KA-Karnataka, KL-Kerala, MP-Madhya Pradesh, MH-Maharashtra, OR-Odisha, PB-Punjab, RJ- Rajasthan, TN-Tamil Nadu, UP-Uttar Pradesh, WB-West Bengal, Smaller states U: AN-A & N Islands, AR-Arunachal Pradesh, CH-Chandigarh, DN-Dadra & Nagar Haveli, DD-Daman & Diu, GA-Goa, HP-Himachal Pradesh, LD-Lakshadweep, ML-Meghalaya, MN-Manipur, MZ-Mizoram, NL-Nagaland, PY-Puducherry, SK-Sikkim, TR-Tripura, UK-Uttarakhand.
Reported in: Suraweera W, Morris SK, Kumar R, Warrell DA, Warrell MJ, et al. (2012) Deaths from Symptomatically Identifiable Furious Rabies in India: A Nationally Representative Mortality Survey. PLoS Negl Trop Dis 6(10): e1847. doi:10.1371/journal.pntd.0001847

Regional variation of estimated rabies deaths and death rates: India, 2005. State wise death rates are standardized to 2005 UN population estimates for India. Total estimated rabies deaths for India in the present study is 12,700, 99% CI (10,000, 15,500). Areas where no rabies deaths captured by this study represent 7% of the total India population. Abbreviations: Larger states U: AP-Andhra Pradesh, AS-Assam, BR-Bihar, CG-Chhattisgarh, DL-Delhi, GJ-Gujarat, HR-Haryana, JK-Jammu & Kashmir, JH-Jharkhand, KA-Karnataka, KL-Kerala, MP-Madhya Pradesh, MH-Maharashtra, OR-Odisha, PB-Punjab, RJ- Rajasthan, TN-Tamil Nadu, UP-Uttar Pradesh, WB-West Bengal, Smaller states U: AN-A & N Islands, AR-Arunachal Pradesh, CH-Chandigarh, DN-Dadra & Nagar Haveli, DD-Daman & Diu, GA-Goa, HP-Himachal Pradesh, LD-Lakshadweep, ML-Meghalaya, MN-Manipur, MZ-Mizoram, NL-Nagaland, PY-Puducherry, SK-Sikkim, TR-Tripura, UK-Uttarakhand.

Reported in: Suraweera W, Morris SK, Kumar R, Warrell DA, Warrell MJ, et al. (2012) Deaths from Symptomatically Identifiable Furious Rabies in India: A Nationally Representative Mortality Survey. PLoS Negl Trop Dis 6(10): e1847. doi:10.1371/journal.pntd.0001847

2 Notes

The spatial distribution of Plasmodium vivax malaria endemicity in 2010.
Panel A shows the 2010 spatial limits of P. vivax malaria risk defined by PvAPI with further medical intelligence, temperature and aridity masks. Areas were defined as stable (dark grey areas, where PvAPI ≥0.1 per 1,000 pa), unstable (medium grey areas, where PvAPI <0.1 per 1,000 pa) or no risk (light grey, where PvAPI = 0 per 1,000 pa). The community surveys of P. vivax prevalence conducted between January 1985 and June 2010 are plotted. The survey data are presented as a continuum of light green to red (see map legend), with zero-valued surveys shown in white. Panel B shows the MBG point estimates of the annual mean PvPR1–99 for 2010 within the spatial limits of stable P. vivax malaria transmission, displayed on the same colour scale. Areas within the stable limits in (A) that were predicted with high certainty (>0.9) to have a PvPR1–99 less than 1% were classed as unstable. Areas in which Duffy negativity gene frequency is predicted to exceed 90% [43] are shown in hatching for additional context.
Published in Gething PW, Elyazar IRF, Moyes CL, Smith DL, Battle KE, et al. (2012) A Long Neglected World Malaria Map: Plasmodium vivax Endemicity in 2010. PLoS Negl Trop Dis 6(9): e1814. doi:10.1371/journal.pntd.0001814

The spatial distribution of Plasmodium vivax malaria endemicity in 2010.

Panel A shows the 2010 spatial limits of P. vivax malaria risk defined by PvAPI with further medical intelligence, temperature and aridity masks. Areas were defined as stable (dark grey areas, where PvAPI ≥0.1 per 1,000 pa), unstable (medium grey areas, where PvAPI <0.1 per 1,000 pa) or no risk (light grey, where PvAPI = 0 per 1,000 pa). The community surveys of P. vivax prevalence conducted between January 1985 and June 2010 are plotted. The survey data are presented as a continuum of light green to red (see map legend), with zero-valued surveys shown in white. Panel B shows the MBG point estimates of the annual mean PvPR1–99 for 2010 within the spatial limits of stable P. vivax malaria transmission, displayed on the same colour scale. Areas within the stable limits in (A) that were predicted with high certainty (>0.9) to have a PvPR1–99 less than 1% were classed as unstable. Areas in which Duffy negativity gene frequency is predicted to exceed 90% [43] are shown in hatching for additional context.

Published in Gething PW, Elyazar IRF, Moyes CL, Smith DL, Battle KE, et al. (2012) A Long Neglected World Malaria Map: Plasmodium vivax Endemicity in 2010. PLoS Negl Trop Dis 6(9): e1814. doi:10.1371/journal.pntd.0001814

2 Notes

Pictorial view of the key elements of our empirical model of human mobility through the air transportation network.
(a) World map with the location of the 1833 airports in the US database from the Federal Aviation Administration (www.faa.gov). (b) Waiting time distributions at connecting and destination airports (from [26]), and at the “home” airport. (c) Illustration of a 1-year travel history of an individual with “home” at San Francisco International Airport (SFO). (d) Graphical representation of the probabilities for exploration and preferential visit of the same individual, after the 1-year “training period.” During exploration the agent visits a new airport while during preferential visit the agent visits a previously-visited place with probability proportional to the frequency of previous visits to that location.
Published in Nicolaides C, Cueto-Felgueroso L, González MC, Juanes R (2012) A Metric of Influential Spreading during Contagion Dynamics through the Air Transportation Network. PLoS ONE 7(7): e40961. doi:10.1371/journal.pone.0040961

Pictorial view of the key elements of our empirical model of human mobility through the air transportation network.

(a) World map with the location of the 1833 airports in the US database from the Federal Aviation Administration (www.faa.gov). (b) Waiting time distributions at connecting and destination airports (from [26]), and at the “home” airport. (c) Illustration of a 1-year travel history of an individual with “home” at San Francisco International Airport (SFO). (d) Graphical representation of the probabilities for exploration and preferential visit of the same individual, after the 1-year “training period.” During exploration the agent visits a new airport while during preferential visit the agent visits a previously-visited place with probability proportional to the frequency of previous visits to that location.

Published in Nicolaides C, Cueto-Felgueroso L, González MC, Juanes R (2012) A Metric of Influential Spreading during Contagion Dynamics through the Air Transportation Network. PLoS ONE 7(7): e40961. doi:10.1371/journal.pone.0040961