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Autumn and winter Santa Ana wind (SAW)-driven wildfires play a substantial role in area burned and societal losses in southern California. Temperature during the event and antecedent precipitation in the week or month prior play a minor role in determining area burned. Burning is dependent on wind intensity and number of human-ignited fires. Over 75% of all SAW events generate no fires; rather, fires during a SAW event are dependent on a fire being ignited. Models explained 40 to 50% of area burned, with number of ignitions being the strongest variable. One hundred percent of SAW fires were human caused, and in the past decade, powerline failures have been the dominant cause. Future fire losses can be reduced by greater emphasis on maintenance of utility lines and attention to planning urban growth in ways that reduce the potential for powerline ignitions.

Dynamic models have been successfully used in producing estimates of HIV epidemics at the national level due to their epidemiological nature and their ability to estimate prevalence, incidence, and mortality rates simultaneously. Recently, HIV interventions and policies have required more information at sub-national levels to support local planning, decision-making and resource allocation. Unfortunately, many areas lack sufficient data for deriving stable and reliable results, and this is a critical technical barrier to more stratified estimates. One solution is to borrow information from other areas within the same country. However, directly assuming hierarchical structures within the HIV dynamic models is complicated and computationally time-consuming. In this article, we propose a simple and innovative way to incorporate hierarchical information into the dynamical systems by using auxiliary data. The proposed method efficiently uses information from multiple areas within each country without increasing the computational burden. As a result, the new model improves predictive ability and uncertainty assessment. [ABSTRACT FROM AUTHOR]

Background: Autumn and winter Santa Ana Winds (SAW) are responsible for the largest and most destructive wildfires in southern California. Aims: (1) To contrast fires ignited on SAW days vs non-SAW days, (2) evaluate the predictive ability of the Canadian Fire Weather Index (CFWI) for these two fire types, and (3) determine climate and weather factors responsible for the largest wildfires. Methods: CAL FIRE (California Department of Forestry and Fire Protection) FRAP (Fire and Resource Assessment Program) fire data were coupled with hourly climate data from four stations, and with regional indices of SAW wind speed, and with seasonal drought data from the Palmer Drought Severity Index. Key results: Fires on non-SAW days were more numerous and burned more area, and were substantial from May to October. CFWI indices were tied to fire occurrence and size for both non-SAW and SAW days, and in the days following ignition. Multiple regression models for months with the greatest area burned explained up to a quarter of variation in area burned. Conclusions: The drivers of fire size differ between non-SAW and SAW fires. The best predictor of fire size for non-SAW fires was drought during the prior 5 years, followed by a current year vapour pressure deficit. For SAW fires, wind speed followed by drought were most important. Fires ignited on Santa Ana Wind days are the largest and most destructive fires but other fires are more numerous and account for greater area burned. Fire indices on days after ignition are important predictors of fire size, but drought and wind speed are most closely tied to fire size. [ABSTRACT FROM AUTHOR]

Introduction: Model-based estimates of key HIV indicators depend on past epidemic trends that are derived based on assumptions about HIV disease progression and mortality in the absence of antiretroviral treatment (ART). Population-based HIV Impact Assessment (PHIA) household surveys conducted between 2015 and 2018 found substantial numbers of respondents living with untreated HIV infection. CD4 cell counts measured in these individuals provide novel information to estimate HIV disease progression and mortality rates off ART. Methods: We used Bayesian multi-parameter evidence synthesis to combine data on (1) cross-sectional CD4 cell counts among untreated adults living with HIV from 10 PHIA surveys, (2) survival after HIV seroconversion in East African seroconverter cohorts, (3) post-seroconversion CD4 counts and (4) mortality rates by CD4 count predominantly from European, North American and Australian seroconverter cohorts. We used incremental mixture importance sampling to estimate HIV natural history and ART uptake parameters used in the Spectrum software. We validated modelled trends in CD4 count at ART initiation against ART initiator cohorts in sub-Saharan Africa. Results: Median untreated HIV survival decreased with increasing age at seroconversion, from 12.5 years [95% credible interval (Crl): 12.1-12.7] at ages 15-24 to 7.2 years (95% Crl: 7.1-7.7) at ages 45-54. Older age was associated with lower initial CD4 counts, faster CD4 count decline and higher HIV-related mortality rates. Our estimates suggested a weaker association between ART uptake and HIV-related mortality rates than previously assumed in Spectrum. Modelled CD4 counts in untreated people living with HIV matched recent household survey data well, though some intercountry variation in frequencies of CD4 counts above 500 cells/[mm3 was not explained. Trends in CD4 counts at ART initiation were comparable to data from ART initiator cohorts. An alternate model that stratified progression and mortality rates by sex did not improve mode fit appreciably. Conclusions: Synthesis of multiple data sources results in similar overall survival as previous Spectrum parameter assumptions but implies more rapid progression and longer survival in lower CD4 categories. New natural history parameter values improve consistency of model estimates with recent cross-sectional CD4 data and trends in CD4 counts at ART initiation. Keywords: adults; cell counts; CD4; disease progression; HIV; statistical model; survival, 1 | INTRODUCTION HIV epidemic models, including the Joint United Nations Programme on HIV/AIDS (UNAIDS)-supported Spectrum model [1], use data on HIV prevalence, HIV diagnoses or HIV-related deaths to estimate [...]